CN105615878B - A kind of fatigue driving eeg monitoring method - Google Patents

Abstract

The invention discloses a kind of fatigue driving eeg monitoring methods, comprising steps of the connection of one, equipment and parameter initialization: EEG signals acquisition device being connect with EEG signals monitoring terminal, and is set to tired step parameter；Two, eeg signal acquires: being acquired and is pre-processed using eeg signal of the EEG signals acquisition device to driver, and eeg signal synchronous driving to EEG signals is monitored terminal；Three, eeg signal analysis is handled: EEG signals monitoring terminal calls eye electricity determination module to the acquisition of EEG signals acquisition device and pretreated eeg signal is analyzed and processed respectively, and process is as follows: number of winks judgment threshold is determining, fatigue driving judges that preceding eeg signal analysis processing and fatigue driving judge to start rear eeg signal analysis processing.The method of the present invention step is simple, design is reasonable and realizes that convenient, using effect is good, can carry out accurate measurements to the fatigue driving state of driver easy, quick, in real time.

Description

A kind of fatigue driving eeg monitoring method

Technical field

The invention belongs to brain wave monitoring technical fields, more particularly, to a kind of fatigue driving eeg monitoring method.

Background technique

With the development of China's economic society, the growth that highway in China road construction is advanced by leaps and bounds, automobile and driver's Quantity is also swift and violent therewith to be increased, and while offering convenience to daily life, the frequent generation of traffic accident is also brought to society Great loss.Nowadays, the various technological means for reducing traffic accident and reduction casualties are all applied and are given birth to, when Preceding at most adopted fatigue detecting means are that driver drives behavioural analysis, i.e., by recording and parsing driver turn direction Disk, the behavioural characteristics such as touch on the brake, and differentiates whether driver is tired；But this mode is influenced pole by driver's driving habit Greatly, ununified, scientific and effective decision theory is supported.Another kind of fatigue detection method is by image analysis means Fatigue Assessment is carried out to driver face and eye feature, such method is analyzed by image variants system currently drives Whether the person of sailing is tired, has certain real-time, but still without general applicability, because everyone biological characteristic is not The same, the external manifestation of somebody's eyes can not represent the state of mind this moment, so there is also very big errors；Separately Outside, the image variants system that such method uses at present mainly includes fatigue driving detecting system, hangers based on ARM Piece formula fatigue alerting device, watch style fatigue driving detecting system, the touch fatigue driving detecting system of steering wheel etc., wherein being based on The fatigue driving detecting system of ARM has a single function, and poor reliability the problem is that system constitutes excessively many and diverse；Hangers piece The function of formula fatigue alerting device is very simple, bows and just alarms, however not necessarily just bows in view of dozing off, and drowsiness causes Feature appearance of bowing ratio it is later, thus real-time is not so good；Watch style fatigue driving detecting system utilizes the bounce of pulse Estimate whether people is tired, not the scientific basis of scientific theory support and authority, and not can solve problem sleeping suddenly； The touch fatigue driving detecting system of steering wheel perceives whether driver holds using some sensors are installed on the steering wheel Steering wheel, and whether driver holds steering wheel and is substantially not directly dependent upon with fatigue state, and makes after installing sensor Steering wheel operation is inconvenient.

In addition, currently used fatigue driving detection means mostly all be drive before or drive after measure, be it is advanced or Lag, and it is non real-time, furthermore it is also very difficult for disposing in the limited space of driver's cabin complicated detecting instrument；And And driver is detached from driver's cabin or does not enter the state of mind of driver's cabin and is different.Therefore, it develops a set of vehicle-mounted, real-time Study in Driver Fatigue State Surveillance System and corresponding fatigue driving monitoring method have become the mesh that domestic and international experts and scholars pursue jointly Mark.

Summary of the invention

In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is that providing a kind of fatigue driving Eeg monitoring method, method and step is simple, design is reasonable and realizes that convenient, using effect is good, can be easy, quick, right in real time The fatigue driving state of driver carries out accurate measurements, and practical value is high.

In order to solve the above technical problems, the technical solution adopted by the present invention is that: a kind of fatigue driving eeg monitoring method, It is characterized in that: method includes the following steps:

Step 1: equipment connection and parameter initialization: EEG signals acquisition device is connect with EEG signals monitoring terminal, And tired step parameter s_c is set by the main control chip that EEG signals monitor terminal；At this point, tired step parameter s_ The numerical value of c is 0；

The EEG signals acquisition device is Mindwave Mobile brain cubic earphone or TGAM module；The brain telecommunications Number monitoring device includes main control chip and the clock circuit connecting respectively with main control chip and warning note unit；

Step 2: eeg signal acquires: using EEG signals acquisition device and according to preset sample frequency pair The eeg signal of driver is acquired and pre-processes, and by pretreated eeg signal synchronous driving to EEG signals Monitor terminal；

It include original eeg signal in the eeg signal, and the sample frequency of the original eeg signal is 512Hz；

Step 3: eeg signal analysis is handled: the main control chip of the EEG signals monitoring terminal calls eye electricity to determine Module is acquired to EEG signals acquisition device and pretreated eeg signal is analyzed and processed, and process is as follows:

Step 3011, number of winks judgment threshold determine: according to sampling time sequencing to EEG signals acquisition device It is acquired in the continuous P second and pretreated eeg signal is analyzed and processed respectively, and according to analysis and processing result to blink Number judgment threshold n₀It is determined；Wherein, P=2 × p, wherein p is positive integer and p >=20；

In this step, to acquisition in EEG signals acquisition device any second and pretreated eeg signal is analyzed When processing, process is as follows:

The synchronous storage of step 30111, eeg signal: to being acquired in the EEG signals acquisition device received at this time one second And pretreated eeg signal synchronizes storage, the eeg signal stored is currently pending eeg signal；

Step 30112, original eeg signal extract and eeg signal energy balane: from described in step 30111 when Original eeg signal is extracted in preceding eeg signal to be processed, and the energy e of currently pending eeg signal is carried out It calculates；

It include 512 original eeg signals, 512 original brains in the currently pending eeg signal The signal value of i-th of original eeg signal is denoted as X in electric wave signal_i；

When calculating the energy e of currently pending eeg signal, according to formula It is counted It calculates；In formula (7), N=512；

Step 30113, blink determine: according to the energy for the currently pending eeg signal being calculated in step 30112 E is measured, is judged whether driver in the second blinks, and obtain the blink decision content bk of this second: as e > E²When, bk=1； Otherwise, bk=0；Wherein, E=280~320；

Step 30114, the synchronous storage of blink decision content: the blink decision content bk obtained in step 30113 is synchronized The analysis treatment process of acquisition and pretreated eeg signal in this second of EEG signals acquisition device is completed in storage；

Step 30115, acquisition and pretreated eeg signal analysis is handled in next second: according to step 30111 to Method described in step 30114, in EEG signals acquisition device lower second acquisition and pretreated eeg signal into Row analysis processing, and obtain the blink decision content bk of this second；

Step 30116 repeats step 30115 P-2 times, until completing to acquire simultaneously in the EEG signals acquisition device continuous P second The analysis treatment process of pretreated eeg signal, and obtain the blink decision content bk of each second；

Step 30117, blink decision content superposition: the blink of each second in the continuous P second obtained in step 30116 is determined Value bk is overlapped, and obtains the sum of blink decision content bkZ；

Step 30118, number of winks judgment threshold determine: according to the sum of the blink decision content obtained in step 30117 Bkz, and according to formulaNumber of winks judgment threshold n is calculated₀；

Wherein, n is positive integer and n >=4；

Also, n × Q=P, wherein Q is positive integer and Q=2~10；

Eeg signal analysis processing, process are as follows before step 3012, fatigue driving judge:

Acquisition and pretreated eeg signal analysis processing in next second after step 30121, judgment threshold determine: step Number of winks judgment threshold n described in rapid 30118₀It is right according to method described in step 30111 to step 30114 after determination It is acquired in EEG signals acquisition device lower second and pretreated eeg signal is analyzed and processed, and obtain blinking for this second Eye decision content bk；

Step 30122 repeats step 30121 n-2 times, until completing number of winks judgment threshold described in step 30118 n₀The analysis treatment process of acquisition and pretreated eeg signal in continuous n-1 seconds of EEG signals acquisition device after determination, And obtain the blink decision content bk of each second；

Step 3013, fatigue driving judgement start rear eeg signal analysis processing: fatigue driving is completed in step 3012 Before judging after eeg signal analysis processing, the eye electricity determination module obtains EEG signals according to sampling time sequencing Device interior acquisition per second and pretreated eeg signal is analyzed and processed respectively, and according to analysis and processing result at this time Whether driver, which is in fatigue driving state, judges；

In this step, the eye electricity determination module is to acquisition and pretreated brain in EEG signals acquisition device any second When electric wave signal is analyzed and processed, process is as follows:

Step 30131, eeg signal analysis processing: right according to method described in step 30111 to step 30114 EEG signals acquisition device acquires at this time and pretreated eeg signal is analyzed and processed, and show that blink at this time is sentenced Definite value bk；

Step 30132, blink decision content superposition: by the blink decision content bk obtained in step 30131 and it is Q-1 second first in respectively The blink decision content bk of second is overlapped, and the sum of the blink decision content of acquisition at this time bkz；

The sum of blink decision content bkz is the blink decision content bk of each second in EEG signals acquisition device is Q seconds continuous Summation；

Step 30133, fatigue driving judgement: it according to the sum of the blink decision content obtained in step 30133 bkz, and combines Identified number of winks judgment threshold n in step 30118₀, sentence to whether driver at this time is in fatigue driving state It is disconnected: as bkz > n₀When, illustrate that driver is in fatigue driving state at this time, the main control chip control warning note unit into Row warning note；Otherwise, illustrate that driver is in normal driving state at this time；

Step 30134, return step 30131, to acquisition and pretreated brain in EEG signals acquisition device lower second Electric wave signal is analyzed and processed.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: carrying out eeg signal analysis processing in step 3 In the process, the main control chip of the EEG signals monitoring terminal also needs to call navigation module is synchronous to obtain the driven vehicle of driver Vehicle essential information, the vehicle essential information includes vehicle geographical location and speed.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: EEG signals acquisition device described in step 1 with It is communicated between EEG signals monitoring device with communication；

The TGAM module includes the EEG signals extraction element extracted to the eeg signal of driver and to brain The extracted signal of electric signal extraction element carries out sampling and pretreated EEG signals pretreatment unit, the EEG signals are located in advance Reason device connects with EEG signals extraction element, and the EEG signals extraction element includes carrying out to the current potential in driver's frontal lobe area First electrode for encephalograms of real-time sampling and the second electrode for encephalograms and third that real-time sampling is carried out to ear's current potential of driver Electrode for encephalograms, first electrode for encephalograms, the second electrode for encephalograms and third electrode for encephalograms with EEG signals pretreatment unit phase It connects；

The EEG signals monitoring device further includes the second wireless communication module to connect respectively with main control chip and fatigue Step parameter zero setting unit；

The EEG signals acquisition device connects with the first wireless communication module, and the EEG signals acquisition device passes through the One wireless communication module and the second wireless communication module are communicated with main control chip.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: EEG signals monitoring terminal is smart phone.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: by EEG signals acquisition device and brain in step 1 When electric signal monitors terminal connection, the main control chip of EEG signals monitoring terminal and host computer need to also be connected with communication It connects；

When main control chip described in step 3025 controls warning note unit progress warning note, the main control chip is synchronous The fatigue driving warning message of the driver is sent to host computer.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: the monitoring of EEG signals described in step 1 terminal is also Including the parameter input unit being connect with main control chip；

After being connect the main control chip of EEG signals monitoring terminal with host computer with communication in step 1, also need Driver's essential information is inputted by parameter input unit, the main control chip deposits the driver's essential information inputted It stores up and by driver's essential information synchronous driving to host computer；

Driver's essential information includes the name and contact method of driver.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: as bkz > n in step 30133₀When, the eye electricity The fatigue driving judging result of determination module is that driver is in fatigue driving state at this time, and main control chip controls warning note Before unit carries out warning note, the main control chip also needs to call brain electricity determination module to the fatigue of the eye electricity determination module It drives judging result to be verified, and according to the verification result of the brain electricity determination module, whether tired is in driver at this time Labor driving condition is determined: when the verification result of the brain electricity determination module is that driver is in fatigue driving state at this time When, determine that driver is in fatigue driving state at this time, the warning note unit of main control chip control later carries out warning note；It is no Then, determine that driver is in normal driving state at this time；

The main control chip calls the brain electricity determination module to the fatigue driving judging result of the eye electricity determination module When being verified, process is as follows:

Step 302-1, the numerical value of tired step parameter s_c tired step parameter zero setting: is set as 0；

Step 302-2, eeg signal analysis is handled: the brain electricity determination module is right according to sampling time sequencing It is acquired in continuous F second of EEG signals acquisition device after tired step parameter zero setting in step 302-1 and pretreated brain wave Signal is analyzed and processed respectively；Wherein, F is positive integer and F=5~15；The brain electricity determination module obtains EEG signals The analysis and processing method of device interior acquisition per second and pretreated eeg signal is all the same；The brain electricity determination module is to brain Acquisition and when pretreated eeg signal is analyzed and processed in electric signal acquisition device any second, process is as follows:

The synchronous storage of step 3021, eeg signal: to being acquired in the EEG signals acquisition device received at this time one second And pretreated eeg signal synchronizes storage；

Step 3022, meditation degree and focus are extracted: extracted from handled eeg signal at this time meditation degree M with it is special Note degree A；

Step 3023, meditation degree correction value and time correcting parameter determine: the current time T provided according to clock circuit, To at this time meditation degree correction value Δ M and time correcting parameter Δ T be determined respectively；

Wherein, when being determined to meditation degree correction value Δ M, when current time T is later than at 6 points and is not later than at 12, Δ M =30~15；When current time T is later than at 12 points and is not later than at 15, Δ M=15~0；When current time T is later than 15 points and not When being later than at 19, Δ M=0~15；It is later than 19 points and not late when current time T is later than at 0 point and is not later than 6 points or current time T When 0, Δ M=0；

When being determined to time correcting parameter Δ T, when current time T is later than at 6 points and is not later than at 12, Δ T=2~ 1.5；When current time T is later than at 12 points and is not later than at 15, Δ T=1.5~1.0；When current time T is later than 15 points and not late When 19, Δ T=1.0~1.5；It is later than 19 points and not late when current time T is later than at 0 point and is not later than 6 points or current time T When 0, Δ T=1.0；

Step 3024, tired step parameter increase and decrease processing: according to the meditation degree M and focus A extracted in step 3022, And identified meditation degree correction value Δ M and time correcting parameter Δ T in step 3023 is combined, to tired step parameter s_ at this time The numerical value of c is increased and decreased processing, and obtains increase and decrease treated tired step parameter s_c: whenWhen, it will be tired The numerical value of labor step parameter s_c adds 1；Otherwise, the numerical value of tired step parameter s_c is judged: as tired step parameter s_c Numerical value be 0 when, the numerical value of tired step parameter s_c remains unchanged；It, will be tired when the numerical value of tired step parameter s_c >=1 The numerical value of step parameter s_c subtracts 1；

Step 3025, fatigue driving judgement: according to the number of treated the tired step parameter s_c of increase and decrease in step 3024 Value, judges whether driver at this time is in fatigue driving state: when increase and decrease treated tired step in step 3024 When the numerical value > N of parameter s_c, judge that driver is in fatigue driving state at this time；Otherwise, judge that driver is at this time Normal driving state；

Wherein, N is preset fatigue driving judgment threshold, and N is positive integer and N=2~8；

Step 3026, verification result obtain: according to the fatigue driving judging result obtained in step 3025, obtaining the brain The verification result of electric determination module: when judging that driver is in fatigue driving state at this time in step 3025, the brain electricity The verification result of determination module is that driver is in fatigue driving state at this time, and the fatigue of the eye electricity determination module is driven in completion Sail the verification process of judging result；Otherwise, it also needs to judge whether to complete to acquire in continuous F seconds of EEG signals acquisition device and locate in advance Whole analysis treatment processes of eeg signal after reason；

Also, acquisition and pretreated brain wave in continuous F seconds of EEG signals acquisition device is completed when judgement obtains When whole analysis treatment processes of signal, the verification result of the brain electricity determination module is that driver is in normal driving shape at this time State；Otherwise, 3026 are entered step；

Step 3026, return step 3021, to acquisition in EEG signals acquisition device lower second and pretreated brain electricity Wave signal is analyzed and processed.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: p=30 described in step 3011；Step 30118 Described in n=30, Q=2；

N=3 described in step 3025.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: using time interval method to meditation in step 3023 When degree correction value Δ M is determined, when current time T is later than at 6 points and is not later than at 15, the time is more late, meditation degree correction value Δ M is smaller；When current time T is later than at 15 points and is not later than at 19, the time is more late, and meditation degree correction value Δ M is bigger；

When being determined using time interval method to time correcting parameter Δ T in step 3023, when current time T is later than 6 When putting and being not later than at 15, the time is more late, and the time, correcting parameter Δ T was smaller；When current time T is later than at 15 points and is not later than at 19 points When, the time is more late, and the time, correcting parameter Δ T was bigger.

A kind of above-mentioned fatigue driving eeg monitoring method, it is characterized in that: being carried out in step 3023 to meditation degree correction value Δ M When determining, process is as follows:

Step A1, current time integral point value determines: being determined to the integral point value of current time T；The clock circuit mentions The current time T of confession is to make for 24 hours, and the integral point value of current time T is denoted as nt, wherein nt be in current time T " when " number Value；

Wherein, nt is integer and nt=0~23；

Step A2, meditation degree correction value Δ M is determined: according to nt identified in step A1, and current time T is combined, it is right Meditation degree correction value Δ M at this time is determined: when current time T is later than at 6 points and is not later than at 12, according to formulaMeditation degree correction value Δ M is calculated；When current time T is later than 12 points and not late When 15, according to formulaMeditation degree correction value Δ M is calculated；When current When quarter T is later than at 15 points and is not later than at 19, according to formulaTo meditation degree correction value Δ M into Row calculates；

In formula (1) and (2), bt1=12；In formula (3), bt2=15；

When being determined in step 3023 to time correcting parameter Δ T, process is as follows:

Step B1, current time integral point value determines: being determined to the integral point value of current time T；The clock circuit mentions The current time T of confession is to make for 24 hours, and the integral point value of current time T is denoted as nt, wherein nt be in current time T " when " number Value；

Wherein, nt is integer and nt=0~23；

Step B2, time correcting parameter Δ T is determined: according to nt identified in step B1, and current time T is combined, it is right Time correcting parameter Δ T at this time is determined: when current time T is later than at 6 points and is not later than at 12, according to formulaTime correcting parameter Δ T is calculated；When current time T is later than 12 points and not When being later than at 15, according to formulaTime correcting parameter Δ T is calculated；When current When moment T is later than at 15 points and is not later than at 19, according to formulaTo time correcting parameter Δ T is calculated；

In formula (1) and (2), bt1=12；In formula (3), bt2=15.

Compared with the prior art, the present invention has the following advantages:

1, method and step is simple, design is reasonable and it is convenient to realize, input cost is lower.

2, fatigue driving eeg monitoring speed is fast, and energy Synchronization Analysis processing obtains the brain electricity condition of driver.

3, used hardware configuration is simple, only includes that EEG signals acquisition device can be real with EEG signals monitoring terminal Existing, wherein EEG signals monitoring terminal can be used smart phone, and structure is simple, small in size and can carry, and uses behaviour Make simplicity, can effectively simplify the operating process of driver.Eeg signal reception and analysis processing can be completed, obtain by only needing mobile phone just Location information is taken the functions such as to communicate with speed, with host computer.Used EEG signals acquisition device and EEG signals monitoring fill The circuit set is simple, design rationally, easy-to-connect and easy to use, input cost is lower, and actual installation laying side Just.

4, only one need to be developed on smart phone is able to achieve answering for presently disclosed fatigue driving eeg monitoring method It is just able to achieve the analysis processing of eeg signal with software, while can will be communicated with host computer.

5, it is matched with navigation module, is based on electronic map, the current speed of driver and geographical location can be obtained in real time, And the current speed and geographical location and fatigue driving eeg monitoring result synchronous driving of driver acquired in energy is to upper Machine (i.e. server) carries out long-range, intelligence by location information and physical fatigue situation of the upper function to driver in this way and supervises Control.

6, safe and reliable, fatigue driving contingency occurrence probability can be effectively reduced, is in fatigue driving when detecting driver When state, EEG signals monitoring terminal can be alarmed in time to remind driver；On the other hand, EEG signals monitor terminal meeting By fatigue driving warning message synchronous driving to host computer, display is synchronized (including in fatigue driving shape by host computer Name and contact method of the driver of state etc.), host computer is according to the contact method of driver, to driving in a manner of call Member is alerted.

7, eye electricity determination module is judged based on energy value, which is the number that EEG signals acquisition device directly exports Value realizes that easy and data processing amount is small, monitoring velocity is fast, and to acquire in Q second and pretreated eeg signal is One judges object, can effectively improve the accuracy of monitoring result.

8, eye electricity determination module is capable of calling using the main control chip of EEG signals monitoring terminal and brain electricity determination module carries out Eye electricity is determined to determine to combine with brain electricity by processing, is overcome that accuracy existing for single judgment mode is lower, speed is slower etc. and is asked Topic all has greatly improved in monitoring accuracy and monitoring velocity.Wherein, based on eye electricity determination module, and sentenced with brain electricity Supplemented by cover half block, rate of false alarm can be effectively reduced.

9, brain electricity determination module synchronizes eeg signal using step fatigue determination method and is analyzed and processed, and right The fatigue driving state of driver carries out real-time judge.Also, when being actually monitored, according to real-time acquisition and pretreated brain Electric wave signal, and the meditation degree correction value of different time and time correcting parameter in one day corresponding is combined to be judged, it drives tired Labor status monitoring result is very accurate, thus verification result is reliable.It is not to immediately arrive at judgement when practical driving fatigue judges As a result, but judged using " benching tunnelling method ", by increase and decrease treated tired step parameter judgement, when tired step is joined When number reaches preset fatigue driving judgment threshold, it just will do it warning note, generally use audio alert mode, such as control Warning note unit processed issues the audio alert information of " please noting that safety ".

10, the decision-making foundation using eeg signal as physiological fatigue, with traditional behavioural characteristic analysis, image procossing Technology etc. has very big inherent advantage, there is the theories integration of E.E.G prestige of science.

11, using effect is good and practical value is high, and economic benefit and social benefit are significant, can the easy fatigue to driver Driving condition carries out real-time monitoring, and can control warning note unit according to monitoring result and carry out warning note, makes driver real When be in clear state, the generation to cut down traffic accidents, thus there is real-time, monitoring effect is good.Also, the present invention is accurate using energy The eeg signal analysis and processing method of fatigue driving is characterized, has been determined that objective fatigue driving detects foundation for driver, has been It further researchs and develops vehicle-mounted, real-time drowsy driving warning system to lay a good foundation, is also traffic management department's science, reasonable Fatigue driving is intervened on ground, reduces artificial traffic accident to greatest extent and provides reliable basis.Meanwhile the present invention is full-featured, tool There is road guide, audio alert, speed prompt, position indicating, host computer the functions such as remotely to monitor, and it is easy to operate, be easy to Universal, low in cost, realization simplicity.

In conclusion the method for the present invention step is simple, design is reasonable and realizes that convenient, using effect is good, it can be easy, quick Accurate measurements are carried out to the fatigue driving state of driver.

Below by drawings and examples, technical scheme of the present invention will be described in further detail.

Detailed description of the invention

Fig. 1 is method flow block diagram of the invention.

Fig. 1-1 is the verification process flow diagram of brain electricity determination module of the present invention.

Fig. 2 is the schematic block circuit diagram of EEG signals acquisition device and EEG signals monitoring device of the present invention.

Fig. 3 is the circuit diagram of EEG signals acquisition device and the first wireless communication module of the present invention.Appended drawing reference is said It is bright:

1-EEG signals acquisition device；1-1-EEG signals extraction element；

The first electrode for encephalograms of 1-11-；The second electrode for encephalograms of 1-12-；1-13-third electrode for encephalograms；

1-2-EEG signals pretreatment unit；2-EEG signals monitoring devices；

2-1-main control chip；2-2-warning note unit；

The second wireless communication module of 2-3-；2-4-parameter input unit；

2-5-fatigue step parameter zero setting unit；2-6-clock circuit；

3-the first wireless communication module；4-host computers.

Specific embodiment

A kind of fatigue driving eeg monitoring method as shown in Figure 1, comprising the following steps:

Step 1: equipment connection and parameter initialization: EEG signals acquisition device 1 and EEG signals monitoring terminal 2 are connected It connects, and tired step parameter s_c is set by the main control chip 2-1 that EEG signals monitor terminal 2；At this point, tired platform The numerical value of rank parameter s_c is 0；

The EEG signals acquisition device 1 is Mindwave Mobile brain cubic earphone or TGAM module；The brain telecommunications Number monitoring device 2 includes main control chip 2-1 and the clock circuit 2-6 and warning note list that connect respectively with main control chip 2-1 First 2-2.

In actual use, the EEG signals monitoring device 2 is located in the driven vehicle of driver.

Step 2: eeg signal acquires: using EEG signals acquisition device 1 and according to preset sample frequency pair The eeg signal of driver is acquired and pre-processes, and by pretreated eeg signal synchronous driving to EEG signals Monitor terminal 2；

It include original eeg signal in the eeg signal, and the sample frequency of the original eeg signal is 512Hz。

Step 3: eeg signal analysis is handled: the main control chip 2-1 of the EEG signals monitoring terminal 2 calls eye electricity Determination module is acquired to EEG signals acquisition device 1 and pretreated eeg signal is analyzed and processed, and process is as follows:

Step 3011, number of winks judgment threshold determine: according to sampling time sequencing to EEG signals acquisition device 1 It is acquired in the continuous P second and pretreated eeg signal is analyzed and processed respectively, and according to analysis and processing result to blink Number judgment threshold n₀It is determined；Wherein, P=2 × p, wherein p is positive integer and p >=20；

In this step, to acquisition in any second of EEG signals acquisition device 1 and pretreated eeg signal divides When analysis processing, process is as follows:

The synchronous storage of step 30111, eeg signal: to being adopted in the EEG signals acquisition device received at this time 1 one seconds Collect and pretreated eeg signal synchronizes storage, the eeg signal stored is currently pending brain wave letter Number；

Step 30112, original eeg signal extract and eeg signal energy balane: from described in step 30111 when Original eeg signal is extracted in preceding eeg signal to be processed, and the energy e of currently pending eeg signal is carried out It calculates；

It include 512 original eeg signals, 512 original brains in the currently pending eeg signal The signal value of i-th of original eeg signal is denoted as X in electric wave signal_i；

When calculating the energy e of currently pending eeg signal, according to formula It is counted It calculates；In formula (7), N=512；

Step 30113, blink determine: according to the energy for the currently pending eeg signal being calculated in step 30112 E is measured, is judged whether driver in the second blinks, and obtain the blink decision content bk of this second: as e > E²When, bk=1； Otherwise, bk=0；Wherein, E=280~320；

Step 30114, the synchronous storage of blink decision content: the blink decision content bk obtained in step 30113 is synchronized The analysis treatment process of acquisition and pretreated eeg signal in 1 this second of EEG signals acquisition device is completed in storage；

Step 30115, acquisition and pretreated eeg signal analysis is handled in next second: according to step 30111 to Method described in step 30114, in EEG signals acquisition device 1 lower second acquisition and pretreated eeg signal into Row analysis processing, and obtain the blink decision content bk of this second；

Step 30116 repeats step 30115 P-2 times, until completing to acquire simultaneously in the 1 continuous P second of EEG signals acquisition device The analysis treatment process of pretreated eeg signal, and obtain the blink decision content bk of each second；

Step 30117, blink decision content superposition: the blink of each second in the continuous P second obtained in step 30116 is determined Value bk is overlapped, and obtains the sum of blink decision content bkZ；

Step 30118, number of winks judgment threshold determine: according to the sum of the blink decision content obtained in step 30117 Bkz, and according to formulaNumber of winks judgment threshold n is calculated₀；

Wherein, n is positive integer and n >=4；

Also, n × Q=P, wherein Q is positive integer and Q=2~10；

Eeg signal analysis processing, process are as follows before step 3012, fatigue driving judge:

Acquisition and pretreated eeg signal analysis processing in next second after step 30121, judgment threshold determine: step Number of winks judgment threshold n described in rapid 30118₀It is right according to method described in step 30111 to step 30114 after determination It is acquired in EEG signals acquisition device 1 lower second and pretreated eeg signal is analyzed and processed, and obtain this second Blink decision content bk；

Step 30122 repeats step 30121 n-2 times, until completing number of winks judgment threshold described in step 30118 n₀The analysis treatment process of acquisition and pretreated eeg signal in EEG signals acquisition device 1 continuous n-1 seconds after determination, And obtain the blink decision content bk of each second；

Step 3013, fatigue driving judgement start rear eeg signal analysis processing: fatigue driving is completed in step 3012 Before judging after eeg signal analysis processing, the eye electricity determination module obtains EEG signals according to sampling time sequencing The interior acquisition per second of device 1 and pretreated eeg signal is analyzed and processed respectively, and according to analysis and processing result to this When driver whether be in fatigue driving state and judge；

In this step, the eye electricity determination module in any second of EEG signals acquisition device 1 acquisition and it is pretreated When eeg signal is analyzed and processed, process is as follows:

Step 30131, eeg signal analysis processing: right according to method described in step 30111 to step 30114 EEG signals acquisition device 1 acquires at this time and pretreated eeg signal is analyzed and processed, and obtains blink at this time Decision content bk；

Step 30132, blink decision content superposition: by the blink decision content bk obtained in step 30131 and it is Q-1 second first in respectively The blink decision content bk of second is overlapped, and the sum of the blink decision content of acquisition at this time bkz；

The sum of blink decision content bkz is the blink decision content bk of each second in EEG signals acquisition device 1 continuous Q seconds Summation；

Step 30133, fatigue driving judgement: it according to the sum of the blink decision content obtained in step 30133 bkz, and combines Identified number of winks judgment threshold n in step 30118₀, sentence to whether driver at this time is in fatigue driving state It is disconnected: as bkz > n₀When, illustrate that driver is in fatigue driving state at this time, the main control chip 2-1 controls warning note unit 2-2 carries out warning note；Otherwise, illustrate that driver is in normal driving state at this time；

Step 30134, return step 30131, to acquisition and pretreated brain in EEG signals acquisition device 1 lower second Electric wave signal is analyzed and processed.

In the present embodiment, with wireless between EEG signals acquisition device described in step 11 and EEG signals monitoring device 2 Communication mode is communicated.

The TGAM module includes the EEG signals extraction element 1-1 extracted to the eeg signal of driver and right The extracted signal of EEG signals extraction element 1-1 carries out sampling and pretreated EEG signals pretreatment unit 1-2, the brain electricity Signal conditioner 1-2 connects with EEG signals extraction element 1-1, and the EEG signals extraction element 1-1 includes to driving The current potential in personnel leaf area carries out the first electrode for encephalograms 1-11 of real-time sampling and is adopted in real time to ear's current potential of driver The the second electrode for encephalograms 1-12 and third electrode for encephalograms 1-13, the first electrode for encephalograms 1-11, the second electrode for encephalograms 1-12 of sample Connect with EEG signals pretreatment unit 1-2 with third electrode for encephalograms 1-13.

Meanwhile the EEG signals monitoring device 2 further includes the second radio communication mold to connect respectively with main control chip 2-1 Block 2-3 and tired step parameter zero setting unit 2-5.In the present embodiment, the fatigue step parameter zero setting unit 2-5 is and master control The keys or buttons of chip connection.

In actual use, the EEG signals acquisition device 1 connects with the first wireless communication module 3, the EEG signals Acquisition device 1 is communicated by the first wireless communication module 3 and the second wireless communication module 2-3 with main control chip 2-1.

The Mindwave Mobile brain cubic earphone or TGAM (ThinkGear AM) module are U.S. NeuroSky (mind reads science and technology) company is that general marketplace applies designed acquiring brain waves and prefinished products.Wherein TGAM module is the U.S. NeuroSky (mind reads science and technology) company is that general marketplace applies designed brain-wave sensor module ASIC, also referred to as TGAM brain electricity mould Block (abbreviation TGAM module).

The Mindwave Mobile brain cubic earphone or TGAM module can handle and export frequency of brain wave spectrum, brain The eSense parameter of electric signal quality, original brain wave and three Neurosky: focus and is blinked at meditation degree (also referred to as allowance) Eye detecting.In actual use, the Mindwave Mobile brain cubic earphone and TGAM module transfer mistake can be obtained by serial ports The data come, the Mindwave Mobile brain cubic earphone and TGAM module send initial data respectively with the frequency of 512Hz It wraps (i.e. original brain wave), and is sent with the frequency of 1Hz through eSense^TMData packet after algorithm process.Due to described The interface of Mindwave Mobile brain cubic earphone and TGAM module and human body only needs a simple stem grafting contact, can be very Readily apply in toy, video-game and health equipment, and since energy consumption is small, is suitable for use in battery powered portable Consumer products using upper.

In actual use, the Mindwave Mobile brain cubic earphone and the acquisition of TGAM module and pretreated brain It include original eeg signal in electric wave signal.Also, (i.e. Mindwave Mobile brain is vertical for the EEG signals acquisition device 1 Square earphone or TGAM module) eeg signal of output is frequency-region signal after Fast Fourier Transform (FFT) (i.e. FFT transform). In the time domain, the original eeg signal is the signal that current potential changes over time, and wherein the unit of current potential is μ V (i.e. microvolt), The unit of time is s.In actual use, the time domain letter of original eeg signal can also be exported with EEG signals acquisition device 1 Number, then Fast Fourier Transform (FFT) is carried out using the control chip of peripheral hardware.

After Fast Fourier Transform (FFT), time-domain signal is transformed to frequency-region signal.For frequency-region signal, independent variable is Frequency, horizontal axis are frequencies, and the longitudinal axis is the amplitude of the frequency signal, the frequency content of expression signal.

After Fast Fourier Transform (FFT), the spectrogram usually said is obtained.Spectrogram describes the frequency structure and frequency of signal The relationship of rate and the frequency signal amplitude.

Herein, the original eeg signal is frequency-region signal, and the signal value of the original eeg signal is signal Amplitude, i.e., the ordinate value being calculated through Fast Fourier Transform (FFT).The original eeg signal is that EEG signals obtain dress 1 signal directly exported is set, direct use is only needed, thus is realized very easy.

1 internal analysis of EEG signals acquisition device exports eeg signal automatically, and handles output Neurosky and obtain The eSense focus and allowance exponent data for obtaining patent, are finally exported by UART interface.This module samples rate is 512Hz, Frequency range 3Hz-100Hz exports the E.E.G original waveform data (i.e. original brain wave data) of 512Hz, the independence of 8 groups of 1Hz Brain wave data and eSense exponent data.Thus, it is acquired in one second and includes 512 institutes in pretreated eeg signal State original eeg signal, corresponding 512 initial data parcels.

The format of each initial data parcel is 04 80 02 xxHigh xxLow xxCheckSum of AA AA, preceding The AA AA 04 80 02 in face be it is constant, rear three bytes change always, and xxHigh and xxLow form initial data rawdata.Thus, it only include a useful data inside an initial data parcel, i.e. rawdata, an initial data is small Packet is exactly an original eeg signal data.Above-mentioned data format, referring to U.S. NeuroSky (mind reads science and technology), company is related Eeg signal data format explanation, this is existing common knowledge.

In actual use, 0XAA 0XAA 0X20 beginning is found in the character string that EEG signals acquisition device 1 exports Character string, that the 3rd character of the character string represents is poorsingle, and what the 31st character represented is meditation degree, the 33rd What character represented is concentration degree (also referred to as focus).

In actual use, the EEG signals monitoring device 2 is laid in the vehicle that driver is driven.The alarm mentions Show that unit 2-2 is voice alerting unit.

As shown in figure 3, the EEG signals pretreatment unit 1-2 is the research and development of U.S. NeuroSky company in the present embodiment TGAM chip.The EEG pin of the output termination TGAM chip of the first electrode for encephalograms 1-11, the second electrode for encephalograms 1-12's The REF pin of output termination TGAM chip, the EEG_GND pin of the output termination TGAM chip of third electrode for encephalograms 1-13.It is real Border is in use, the second electrode for encephalograms 1-12 is reference electrode.

In actual use, the end EEG of the TGAM chip inputs the brain telecommunications that the first electrode for encephalograms 1-11 is sampled Number, the effect at the end EEG_shiled be shielding before the sampled EEG signals of the first electrode for encephalograms 1-11 input TGAM chip this The interference of section time；The end REF inputs the EEG signals that the second electrode for encephalograms 1-2 is sampled, and the second electrode for encephalograms 1-12 is adopted Ear's EEG signals of sample can effectively filter out self start type brain wave as reference potential；The end REF_shiled mainly shields the The interference of this period before the sampled EEG signals input TGAM chip of two electrode for encephalograms 1-12；E.E.G ground wire is also connected to people The EEG signals that the ear of body, i.e. third electrode for encephalograms 1-13 are sampled, main effect is to shield human body head or less The influence of electric wave, for example electrocardio wave is exactly a kind of stronger interference wave, and the connection of E.E.G ground wire can effectively filter out electrocardio wave.? That is third electrode for encephalograms 1-13 is the electrode for acquiring brain wave ground signalling.

In the present embodiment, first wireless communication module 3 and the second wireless communication module 2-3 are Bluetooth wireless communication Module.Also, the Bluetooth wireless communication module is HL-MD08R-C2A module.In actual use, first wireless communication Module 3 and the second wireless communication module 2-3 can also use other types of wireless communication module.

In the present embodiment, the first electrode for encephalograms 1-11 is placed on according to 10-and what 20 system electrode placement methods determined drives On the left antinion for the person of sailing, the second electrode for encephalograms 1-12 and third electrode for encephalograms 1-13 are both placed according to 10-20 systems electricity On in the left temporo for the driver that pole placement methods determine.Wherein, 10-20 system electrode placement methods, i.e., international electroencephalography can provide Normal electrode placement methods.Thus, what EEG signals extraction element 1-1 was mainly acquired is prefrontal area, specifically left antinion (F_P1) Current potential in this electrode site.The second electrode for encephalograms 1-12 and third electrode for encephalograms 1-13 are both placed in left temporo (T3) In this electrode site.

In the present embodiment, the model TGAM1 of the TGAM chip, first wireless communication module 3 and second is wireless Communication module 2-3 is BlueTooth chip.When physical cabling, the TXD pin and the first radio communication mold of the TGAM chip The RX pin of block 3 connects.The power end of the TGAM chip and the VCC pin of TGAM chip connect+3.3V power end.

In the present embodiment, the EEG signals monitoring terminal 2 further includes the display unit connecting with main control chip 2-1.It is real In the use process of border, display is synchronized to information such as the meditation degree M and focus A of driver by the display unit, side Just user dynamically surveys the electrical activity of brain state of oneself.

In the present embodiment, as bkz > n in step 30133₀When, the fatigue driving judging result of the eye electricity determination module Be in fatigue driving state for driver at this time, and main control chip 2-1 control warning note unit 2-2 carry out warning note it Before, the main control chip 2-1 also need to call brain electricity determination module to the fatigue driving judging result of the eye electricity determination module into Row verifying, and according to the verification result of the brain electricity determination module, it is carried out to whether driver at this time is in fatigue driving state Determine: when the verification result of the brain electricity determination module is that driver is in fatigue driving state at this time, determination drives at this time Member is in fatigue driving state, and main control chip 2-1 controls warning note unit 2-2 and carries out warning note later；Otherwise, it determines this When driver be in normal driving state；

As Figure 1-1, the main control chip 2-1 calls the brain electricity determination module to the tired of the eye electricity determination module When please sailing judging result and being verified, process is as follows:

Step 302-1, the numerical value of tired step parameter s_c tired step parameter zero setting: is set as 0；

Step 302-2, eeg signal analysis is handled: the brain electricity determination module is right according to sampling time sequencing It is acquired in EEG signals acquisition device 1 continuous F second after tired step parameter zero setting in step 302-1 and pretreated brain wave Signal is analyzed and processed respectively；Wherein, F is positive integer and F=5~15；The brain electricity determination module obtains EEG signals The analysis and processing method of the interior acquisition per second of device 1 and pretreated eeg signal is all the same；The brain electricity determination module pair Acquisition and when pretreated eeg signal is analyzed and processed in any second of EEG signals acquisition device 1, process is as follows:

The synchronous storage of step 3021, eeg signal: to being acquired in the EEG signals acquisition device received at this time 1 one seconds And pretreated eeg signal synchronizes storage；

Step 3022, meditation degree and focus are extracted: extracted from handled eeg signal at this time meditation degree M with it is special Note degree A；

Step 3023, meditation degree correction value and time correcting parameter determine: the current time provided according to clock circuit 2-6 T, at this time meditation degree correction value Δ M and time correcting parameter Δ T be determined respectively；

Wherein, when being determined to meditation degree correction value Δ M, when current time T is later than at 6 points and is not later than at 12, Δ M =30~15；When current time T is later than at 12 points and is not later than at 15, Δ M=15~0；When current time T is later than 15 points and not When being later than at 19, Δ M=0~15；It is later than 19 points and not late when current time T is later than at 0 point and is not later than 6 points or current time T When 0, Δ M=0；

When being determined to time correcting parameter Δ T, when current time T is later than at 6 points and is not later than at 12, Δ T=2~ 1.5；When current time T is later than at 12 points and is not later than at 15, Δ T=1.5~1.0；When current time T is later than 15 points and not late When 19, Δ T=1.0~1.5；It is later than 19 points and not late when current time T is later than at 0 point and is not later than 6 points or current time T When 0, Δ T=1.0；

Step 3024, tired step parameter increase and decrease processing: according to the meditation degree M and focus A extracted in step 3022, And identified meditation degree correction value Δ M and time correcting parameter Δ T in step 3023 is combined, to tired step parameter s_ at this time The numerical value of c is increased and decreased processing, and obtains increase and decrease treated tired step parameter s_c: whenWhen, it will be tired The numerical value of labor step parameter s_c adds 1；Otherwise, the numerical value of tired step parameter s_c is judged: as tired step parameter s_c Numerical value be 0 when, the numerical value of tired step parameter s_c remains unchanged；It, will be tired when the numerical value of tired step parameter s_c >=1 The numerical value of step parameter s_c subtracts 1；

Step 3025, fatigue driving judgement: according to the number of treated the tired step parameter s_c of increase and decrease in step 3024 Value, judges whether driver at this time is in fatigue driving state: when increase and decrease treated tired step in step 3024 When the numerical value > N of parameter s_c, judge that driver is in fatigue driving state at this time；Otherwise, judge that driver is at this time Normal driving state；

Wherein, N is preset fatigue driving judgment threshold, and N is positive integer and N=2~8；

Step 3026, verification result obtain: according to the fatigue driving judging result obtained in step 3025, obtaining the brain The verification result of electric determination module: when judging that driver is in fatigue driving state at this time in step 3025, the brain electricity The verification result of determination module is that driver is in fatigue driving state at this time, and the fatigue of the eye electricity determination module is driven in completion Sail the verification process of judging result；Otherwise, it also needs to judge whether to complete to acquire in EEG signals acquisition device 1 continuous F second and pre- Treated, and the whole of eeg signal analyze treatment processes；

Also, acquisition and pretreated brain wave in EEG signals acquisition device 1 continuous F seconds is completed when judgement obtains When whole analysis treatment processes of signal, the verification result of the brain electricity determination module is that driver is in normal driving shape at this time State；Otherwise, 3026 are entered step；

Step 3026, return step 3021, to acquisition in EEG signals acquisition device 1 lower second and pretreated brain electricity Wave signal is analyzed and processed.

In actual use, when the eye electricity determination module judges that driver is in fatigue driving state at this time, institute State main control chip 2-1 recall the brain electricity determination module to also need call brain electricity determination module to the eye electricity determination module Fatigue driving judging result is verified, thus can effectively reduce rate of failing to report, overcomes the existing monitoring of single judgment method accurate The problem of property and monitoring velocity.On the other hand, the monitoring accuracy of the eye electricity determination module and the brain electricity determination module is equal It is higher.

Wherein, being later than does not include 6 points in 6 points, and being not later than at 12 points includes 12 points；Being later than does not include 12 points in 12 points, not late It include 15 points in 15 points；Being later than does not include 15 points in 15 points, and being not later than includes 19 points in 19 points.

The energy e for the currently pending eeg signal being calculated in step 30112 is the parameter of reflection blink intensity, The numerical value of energy e is bigger, and blink intensity is bigger.Herein, determine to work as e > E²When, illustrate that blink intensity has reached the journey of blink Primary blink artificially occurs at this time for degree.

In the present embodiment, N=3 described in step 3025.

In actual use, according to specific needs, the value size of N is adjusted accordingly.

In the present embodiment, p=30 described in step 3011；N=30 described in step 30118, Q=2.

In actual use, according to specific needs, the value size of p and Q is adjusted accordingly.

In actual use, when being determined using time interval method to meditation degree correction value Δ M in step 3023, when When current time T is later than at 6 points and is not later than at 15, the time is more late, and meditation degree correction value Δ M is smaller；When current time T is later than 15 When putting and being not later than at 19, the time is more late, and meditation degree correction value Δ M is bigger；

When being determined using time interval method to time correcting parameter Δ T in step 3023, when current time T is later than 6 When putting and being not later than at 15, the time is more late, and the time, correcting parameter Δ T was smaller；When current time T is later than at 15 points and is not later than at 19 points When, the time is more late, and the time, correcting parameter Δ T was bigger.

Thus, the meditation degree correction value Δ M and time correcting parameter Δ T that the present invention uses is obtain according to current time Dynamic value, design rationally, can effectively improve fatigue driving monitoring accuracy.The original of above-mentioned setting is carried out to meditation degree correction value Δ M Because being: will be gradually in a state of fatigue from 6 points to 12 human bodies of the morning, meditation degree is gradually increased, and reduces the amendment of meditation degree Value Δ M also complies with rule；When point being that people is most tired in one day from 12 points to 15, meditation degree correction value Δ M can also reduce；From 15 points to 19 people have slowly restored spirit again, thus meditation degree correction value Δ M can slowly increase again；Other time default meditation Spending correction value Δ M is 0.Thus, the determination process of meditation degree correction value Δ M is simple, reasonable and using effect is good.

Correspondingly, when fatigue, meditation degree and focus will appear multiple proportion, but can change with time and Variation, for multiple proportion nearly all between 1 to 2, this coefficient can be with the proportional growth of the fatigue state of human body.Together Reason, 6:00 AM is to 12 points of the morning, and the time, correcting parameter Δ T can reduce, and value range is between 2.0~1.5；From 12 points to 15 Point, the time, correcting parameter Δ T can also reduce, and value range is 1.5~1.0；From 15 points to 19 point, time correcting parameter Δ T meeting Increase, value range is 1.0~1.5.

To realize simplicity and further increasing detection accuracy, in the present embodiment, to meditation degree correction value in step 3023 When Δ M is determined, process is as follows:

Step A1, current time integral point value determines: being determined to the integral point value of current time T；The clock circuit 2-6 The current time T of offer is to make for 24 hours, and the integral point value of current time T is denoted as nt, wherein nt be in current time T " when " number Value；

Wherein, nt is integer and nt=0~23；

Step A2, meditation degree correction value Δ M is determined: according to nt identified in step A1, and current time T is combined, it is right Meditation degree correction value Δ M at this time is determined: when current time T is later than at 6 points and is not later than at 12, according to formulaMeditation degree correction value Δ M is calculated；When current time T is later than 12 points and not late When 15, according to formulaMeditation degree correction value Δ M is calculated；When current When quarter T is later than at 15 points and is not later than at 19, according to formulaTo meditation degree correction value Δ M into Row calculates；

In formula (1) and (2), bt1=12；In formula (3), bt2=15.

In the present embodiment, when being determined in step 3023 to time correcting parameter Δ T, process is as follows:

Step B1, current time integral point value determines: being determined to the integral point value of current time T；The clock circuit 2-6 The current time T of offer is to make for 24 hours, and the integral point value of current time T is denoted as nt, wherein nt be in current time T " when " number Value；

Wherein, nt is integer and nt=0~23；

Step B2, time correcting parameter Δ T is determined: according to nt identified in step B1, and current time T is combined, it is right Time correcting parameter Δ T at this time is determined: when current time T is later than at 6 points and is not later than at 12, according to formulaTime correcting parameter Δ T is calculated；When current time T is later than 12 points and not When being later than at 15, according to formulaTime correcting parameter Δ T is calculated；When current When moment T is later than at 15 points and is not later than at 19, according to formulaTo time correcting parameter Δ T is calculated；

In formula (1) and (2), bt1=12；In formula (3), bt2=15.

In the present embodiment, current time T is denoted as nt:fz, wherein nt be current time T in " when " numerical value, fz is current The numerical value " divided " in moment T.Thus, nt and fz be respectively in current time T " when " and " dividing " numerical value, directly read.

In the present embodiment, when connecting EEG signals acquisition device 1 with EEG signals monitoring terminal 2 in step 1, also need The main control chip 2-1 of EEG signals monitoring terminal 2 is connect with host computer 4 with communication；

When main control chip 2-1 described in step 305 controls warning note unit 2-2 progress warning note, the master control core The synchronous fatigue driving warning message that the driver is sent to host computer 4 of piece 2-1.

In actual use, it is attached between the main control chip 2-1 and host computer 4 with communication.

In the present embodiment, it further includes the parameter connecting with main control chip 2-1 that EEG signals described in step 1, which monitor terminal 2, Input unit 2-4.

The main control chip 2-1 of EEG signals monitoring terminal 2 is connect with host computer 4 with communication in step 1 Afterwards, it also needs to input driver's essential information by parameter input unit 2-4, the main control chip 2-1 is to the driver inputted Essential information carries out storage and by driver's essential information synchronous driving to host computer 4.

Driver's essential information includes the name and contact method of driver.

In the present embodiment, the EEG signals monitoring terminal 2 is smart phone.

In actual use, the EEG signals monitoring terminal 2 can also use other types of data processing terminal, such as slap Upper computer, ipad etc..

It is carried out in the present embodiment, in step 3 in eeg signal analysis treatment process, the EEG signals monitor terminal 2 Main control chip 2-1 also need to call the synchronous vehicle essential information for obtaining the driven vehicle of driver of navigation module, the vehicle Essential information includes vehicle geographical location and speed.

Also, the main control chip 2-1 can control warning note unit 2-2, and (such as 5s) casting is once worked as at regular intervals Preceding locating geographical location is not necessarily to divert one's attention to see the mobile phone map when driving and unsafe accident occurs.

In actual use, the vehicle mounted guidance software that the navigation module can also use, such as high moral navigation software.Together When, when judging that driver is in driving fatigue state, what the main control chip 2-1 navigation module synchronization obtained is driven Sail the vehicle essential information of vehicle, driver and the monitoring personnel remotely monitored by host computer 4 can be to driver at Position when fatigue state is accurately held, in this way when driver, which drives vehicle appearance position, to be changed, host computer 4 or brain Electric signal monitors terminal 2 and can issue voice prompting by warning note unit 2-2 or play music, is pierced again to driver Swash；Also, remote monitoring personnel can also remind driver by way of mobile phone communication, and driver is reminded to be at this time It is no to meet safe driving requirement.

In addition, driver presses tired step parameter zero setting unit 2-5 after driver currently switchs to waking state, lead to It crosses main control chip 2-1 and the numerical value of tired step parameter s_c is set as 0.

It, need to also be from handled brain wave at this time before carrying out meditation degree and focus extraction in the present embodiment, in step 302 Poorsingle is extracted in signal, and according to the numerical value of poorsingle to the wearing appearance of EEG signals acquisition device 1 at this time Whether gesture is correctly judged: when the numerical value of poorsingle is greater than 200, illustrating the wearing appearance of EEG signals acquisition device 1 Gesture is incorrect；Otherwise, illustrate the wearing correct set of EEG signals acquisition device 1.

In actual use, the warning note unit 2-2 is voice alerting unit.According to the numerical value of poorsingle It is complete by the main control chip 2-1 to the process whether the wearing posture of EEG signals acquisition device 1 at this time is correctly judged At when judging the wearing fault of EEG signals acquisition device 1, warning note unit 2-2 issues voice prompting, such as " your wearing posture is wrong " voice broadcast, more can make detection process more accurate in this way, prevent because not wearing ear Machine leads to the outflow of wrong data.Poorsingle is the data that EEG signals acquisition device 1 directly exports, and need to only be extracted i.e. It can.

The above is only presently preferred embodiments of the present invention, is not intended to limit the invention in any way, it is all according to the present invention Technical spirit any simple modification to the above embodiments, change and equivalent structural changes, still fall within skill of the present invention In the protection scope of art scheme.

Claims (8)

1. a kind of fatigue driving eeg monitoring method, it is characterised in that: method includes the following steps:

Step 1: equipment connection and parameter initialization: even by EEG signals acquisition device (1) and EEG signals monitoring terminal (2) It connects, and tired step parameter s_c is set by the main control chip (2-1) of EEG signals monitoring terminal (2)；At this point, tired The numerical value of labor step parameter s_c is 0；

The EEG signals acquisition device (1) is Mindwave Mobile brain cubic earphone or TGAM module；The EEG signals Monitoring device (2) includes main control chip (2-1) and the clock circuit (2-6) connecting respectively with main control chip (2-1) and alarm Prompt unit (2-2)；

Step 2: eeg signal acquires: using EEG signals acquisition device (1) and according to preset sample frequency to driving The eeg signal for the person of sailing is acquired and pre-processes, and pretreated eeg signal synchronous driving to EEG signals is supervised It surveys terminal (2)；

It include original eeg signal in the eeg signal, and the sample frequency of the original eeg signal is 512Hz； The eeg signal of EEG signals acquisition device (1) output is the frequency-region signal after Fast Fourier Transform (FFT)；

Step 3: eeg signal analysis is handled: the main control chip (2-1) of EEG signals monitoring terminal (2) calls eye electricity Determination module is acquired to EEG signals acquisition device (1) and pretreated eeg signal is analyzed and processed, and process is as follows:

Step 3011, number of winks judgment threshold determine: connecting according to sampling time sequencing to EEG signals acquisition device (1) It is acquired in P seconds continuous and pretreated eeg signal is analyzed and processed respectively, and according to analysis and processing result to blink time Number judgment threshold n₀It is determined；Wherein, P=2 × p, wherein p is positive integer and p >=20；

In this step, to acquisition in EEG signals acquisition device (1) any second and pretreated eeg signal is analyzed When processing, process is as follows:

The synchronous storage of step 30111, eeg signal: to being acquired in the EEG signals acquisition device received at this time (1) one second And pretreated eeg signal synchronizes storage, the eeg signal stored is currently pending eeg signal；

Step 30112, original eeg signal extract and eeg signal energy balane: from described in step 30111 currently to Original eeg signal is extracted in processing eeg signal, and the energy e of currently pending eeg signal is calculated；

It include 512 original eeg signals, 512 original brain waves in the currently pending eeg signal The signal value of i-th of original eeg signal is denoted as X in signal_i；

When calculating the energy e of currently pending eeg signal, according to formula(7) it is calculated；It is public In formula (7), N=512；

Step 30113, blink determine: according to the energy e for the currently pending eeg signal being calculated in step 30112, Judge whether driver in the second blinks, and obtain the blink decision content bk of this second: as e > E²When, bk=1；Otherwise, Bk=0；Wherein, E=280~320；

Step 30114, the synchronous storage of blink decision content: synchronizing storage to the blink decision content bk obtained in step 30113, Complete the analysis treatment process of acquisition and pretreated eeg signal in EEG signals acquisition device (1) this second；

Acquisition and pretreated eeg signal analysis processing in step 30115, next second: according to step 30111 to step Method described in 30114, to acquisition in EEG signals acquisition device (1) next second and pretreated eeg signal carries out Analysis processing, and obtain the blink decision content bk of this second；

Step 30116, P-2 times repeat step 30115, until complete EEG signals acquisition device (1) the continuous P second in acquisition and it is pre- The analysis treatment process of treated eeg signal, and obtain the blink decision content bk of each second；

Step 30117, blink decision content superposition: to the blink decision content bk of each second in the continuous P second obtained in step 30116 It is overlapped, and obtains the sum of blink decision content bkZ；

Step 30118, number of winks judgment threshold determine: according to the sum of blink decision content obtained in step 30117 bkz, And according to formulaNumber of winks judgment threshold n is calculated₀；

Wherein, n is positive integer and n >=4；

Also, n × Q=P, wherein Q is positive integer and Q=2~10；

Eeg signal analysis processing, process are as follows before step 3012, fatigue driving judge:

Acquisition and pretreated eeg signal analysis processing in next second after step 30121, judgment threshold determine: step The n of number of winks judgment threshold described in 30118₀After determination, according to method described in step 30111 to step 30114, to brain It is acquired in electric signal acquisition device (1) next second and pretreated eeg signal is analyzed and processed, and obtain this second Blink decision content bk；

Step 30122 repeats step 30121 n-2 times, until completing number of winks judgment threshold n described in step 30118₀It determines The analysis treatment process of EEG signals acquisition device (1) interior acquisition in continuous n-1 seconds and pretreated eeg signal afterwards, and obtain Obtain the blink decision content bk of each second；

Step 3013, fatigue driving judgement start rear eeg signal analysis processing: fatigue driving judgement is completed in step 3012 After preceding eeg signal analysis processing, the eye electricity determination module is according to sampling time sequencing to EEG signals acquisition device (1) interior acquisition per second and pretreated eeg signal is analyzed and processed respectively, and according to analysis and processing result at this time Whether driver, which is in fatigue driving state, judges；

In this step, the eye electricity determination module is to acquisition and pretreated brain in EEG signals acquisition device (1) any second When electric wave signal is analyzed and processed, process is as follows:

Step 30131, eeg signal analysis processing: according to method described in step 30111 to step 30114, to brain electricity Signal acquisition device (1) acquires at this time and pretreated eeg signal is analyzed and processed, and show that blink at this time is sentenced Definite value bk；

Step 30132, blink decision content superposition: by the blink decision content bk obtained in step 30131 and it is Q-1 seconds first in each second Blink decision content bk is overlapped, and the sum of the blink decision content of acquisition at this time bkz；

The sum of blink decision content bkz is the total of the blink decision content bk of each second in EEG signals acquisition device (1) is Q seconds continuous With；

Step 30133, fatigue driving judgement: according to the sum of the blink decision content obtained in step 30133 bkz, and step is combined Identified number of winks judgment threshold n in 30118₀, judge whether driver at this time is in fatigue driving state: when Bkz > n₀When, illustrate that driver is in fatigue driving state at this time, the main control chip (2-1) controls warning note unit (2- 2) warning note is carried out；Otherwise, illustrate that driver is in normal driving state at this time；

Step 30134, return step 30131, to acquisition in EEG signals acquisition device (1) next second and pretreated brain electricity Wave signal is analyzed and processed；

It is carried out in step 3 in eeg signal analysis treatment process, the main control chip (2- of EEG signals monitoring terminal (2) 1) it also needs to call the synchronous vehicle essential information for obtaining the driven vehicle of driver of navigation module, the vehicle essential information includes Vehicle geographical location and speed.

2. a kind of fatigue driving eeg monitoring method described in accordance with the claim 1, it is characterised in that: the electricity of brain described in step 1 It is communicated between signal acquisition device (1) and EEG signals monitoring device (2) with communication；

The TGAM module includes the EEG signals extraction element (1-1) extracted to the eeg signal of driver and to brain Electric signal extraction element (1-1) extracted signal carries out sampling and pretreated EEG signals pretreatment unit (1-2), the brain Electric signal pretreatment unit (1-2) connects with EEG signals extraction element (1-1), EEG signals extraction element (1-1) packet It includes and the first electrode for encephalograms (1-11) of real-time sampling is carried out to the current potential in driver's frontal lobe area and to ear's current potential of driver The second electrode for encephalograms (1-12) and third electrode for encephalograms (1-13) of progress real-time sampling, first electrode for encephalograms (1-11), Second electrode for encephalograms (1-12) and third electrode for encephalograms (1-13) connect with EEG signals pretreatment unit (1-2)；

The EEG signals monitoring device (2) further includes the second wireless communication module (2- to connect respectively with main control chip (2-1) And tired step parameter zero setting unit (2-5) 3)；

The EEG signals acquisition device (1) connects with the first wireless communication module (3), the EEG signals acquisition device (1) It is communicated by the first wireless communication module (3) and the second wireless communication module (2-2) with main control chip (2-1).

3. a kind of fatigue driving eeg monitoring method according to claim 1 or 2, it is characterised in that: the EEG signals Monitoring terminal (2) is smart phone.

4. a kind of fatigue driving eeg monitoring method described in accordance with the claim 1, it is characterised in that: the electricity of brain described in step 1 Signal monitoring terminal (2) further includes the parameter input unit (2-4) connecting with main control chip (2-1)；

The main control chip (2-1) of EEG signals monitoring terminal (2) is connect with host computer (4) with communication in step 1 Afterwards, it also needs to input driver's essential information by parameter input unit (2-4), the main control chip (2-1) is driven to what is inputted The person's of sailing essential information carries out storage and by driver's essential information synchronous driving to host computer (4)；

Driver's essential information includes the name and contact method of driver.

5. a kind of fatigue driving eeg monitoring method according to claim 1 or 2, it is characterised in that: in step 30133 when Bkz > n₀When, the fatigue driving judging result of the eye electricity determination module is that driver is in fatigue driving state, and master at this time Before controlling chip (2-1) control warning note unit (2-2) progress warning note, the main control chip (2-1) also needs to call brain Electric determination module verifies the fatigue driving judging result of the eye electricity determination module, and according to the brain electricity determination module Verification result, whether driver at this time is determined in fatigue driving state: when the verifying of the brain electricity determination module As a result for when driver is in fatigue driving state at this time, determining driver at this time is in fatigue driving state, later master control core Piece (2-1) controls warning note unit (2-2) and carries out warning note；Otherwise, it determines driver is in normal driving state at this time；

The main control chip (2-1) calls the brain electricity determination module to the fatigue driving judging result of the eye electricity determination module When being verified, process is as follows:

Step 302-1, the numerical value of tired step parameter s_c tired step parameter zero setting: is set as 0；

Step 302-2, eeg signal analysis is handled: the brain electricity determination module is according to sampling time sequencing, to step EEG signals acquisition device (1) continuously acquires in F seconds after tired step parameter zero setting in 302-1 and pretreated brain wave is believed It number is analyzed and processed respectively；Wherein, F is positive integer and F=5~15；The brain electricity determination module obtains EEG signals and fills The analysis and processing method for setting (1) interior acquisition per second and pretreated eeg signal is all the same；The brain electricity determination module pair Acquisition and when pretreated eeg signal is analyzed and processed in EEG signals acquisition device (1) any second, process is as follows:

The synchronous storage of step 3021, eeg signal: to being acquired simultaneously in the EEG signals acquisition device received at this time (1) one second Pretreated eeg signal synchronizes storage；

Step 3022, meditation degree and focus are extracted: extracting meditation degree M and focus from handled eeg signal at this time A；

Step 3023, meditation degree correction value and time correcting parameter determine: the current time T provided according to clock circuit (2-6), To at this time meditation degree correction value Δ M and time correcting parameter Δ T be determined respectively；

Wherein, when being determined to meditation degree correction value Δ M, when current time T is later than at 6 points and is not later than at 12, Δ M=30 ~15；When current time T is later than at 12 points and is not later than at 15, Δ M=15~0；When current time T is later than at 15 points and is not later than At 19, Δ M=0~15；It is later than at 19 points when current time T is later than at 0 point and is not later than 6 points or current time T and is not later than at 0 point When, Δ M=0；

When being determined to time correcting parameter Δ T, when current time T is later than at 6 points and is not later than at 12, Δ T=2~1.5； When current time T is later than at 12 points and is not later than at 15, Δ T=1.5~1.0；When current time T is later than at 15 points and is not later than 19 When point, Δ T=1.0~1.5；It is later than at 19 points when current time T is later than at 0 point and is not later than 6 points or current time T and is not later than 0 When point, Δ T=1.0；

Step 3024, tired step parameter increase and decrease processing: it according to the meditation degree M and focus A extracted in step 3022, and ties Identified meditation degree correction value Δ M and time correcting parameter Δ T in step 3023 is closed, to tired step parameter s_c's at this time Numerical value is increased and decreased processing, and obtains increase and decrease treated tired step parameter s_c: whenWhen, by tired platform The numerical value of rank parameter s_c adds 1；Otherwise, the numerical value of tired step parameter s_c is judged: when the number of tired step parameter s_c When value is 0, the numerical value of tired step parameter s_c is remained unchanged；When the numerical value of tired step parameter s_c >=1, by tired step The numerical value of parameter s_c subtracts 1；

Step 3025, fatigue driving judgement: right according to the numerical value of treated the tired step parameter s_c of increase and decrease in step 3024 Whether driver is in fatigue driving state and judges at this time: as increase and decrease treated tired step parameter s_ in step 3024 When the numerical value > N of c, judge that driver is in fatigue driving state at this time；Otherwise, judge that driver is at this time normally to drive Sail state；

Wherein, N is preset fatigue driving judgment threshold, and N is positive integer and N=2~8；

Step 3026, verification result obtain: according to the fatigue driving judging result obtained in step 3025, obtaining the brain electricity and sentence The verification result of cover half block: when judging that driver is in fatigue driving state at this time in step 3025, the brain electricity determines The verification result of module is that driver is in fatigue driving state at this time, and the fatigue driving of the eye electricity determination module is sentenced in completion The verification process of disconnected result；Otherwise, it also needs to judge whether to complete EEG signals acquisition device (1) interior acquisition in continuous F seconds and locate in advance Whole analysis treatment processes of eeg signal after reason；

Also, acquisition and pretreated brain wave letter in EEG signals acquisition device (1) continuous F second is completed when judgement obtains Number whole analysis treatment process when, the verification result of the brain electricity determination module is that driver is in normal driving shape at this time State；Otherwise, 3026 are entered step；

Step 3026, return step 3021, to acquisition and pretreated brain wave in EEG signals acquisition device (1) next second Signal is analyzed and processed.

6. a kind of fatigue driving eeg monitoring method according to claim 5, it is characterised in that: described in step 3011 P=30；N=30 described in step 30118, Q=2；

N=3 described in step 3025.

7. a kind of fatigue driving eeg monitoring method according to claim 5, it is characterised in that: when being used in step 3023 Between interval method when being determined to meditation degree correction value Δ M, when current time T is later than at 6 points and is not later than at 15, the time is got over Evening, meditation degree correction value Δ M are smaller；When current time T is later than at 15 points and is not later than at 19, the time is more late, the amendment of meditation degree It is bigger to be worth Δ M；

In step 3023 using time interval method time correcting parameter Δ T is determined when, when current time T be later than 6 points and When being not later than at 15, the time is more late, and the time, correcting parameter Δ T was smaller；When current time T is later than at 15 points and is not later than at 19, Time is more late, and the time, correcting parameter Δ T was bigger.

8. a kind of fatigue driving eeg monitoring method according to claim 5, it is characterised in that: to meditation in step 3023 When degree correction value Δ M is determined, process is as follows:

Step A1, current time integral point value determines: being determined to the integral point value of current time T；The clock circuit (2-6) mentions The current time T of confession is to make for 24 hours, and the integral point value of current time T is denoted as nt, wherein nt be in current time T " when " number Value；

Wherein, nt is integer and nt=0~23；

Step A2, meditation degree correction value Δ M is determined: according to nt identified in step A1, and current time T is combined, at this time Meditation degree correction value Δ M be determined: when current time T is later than at 6 points and is not later than at 12, according to formulaMeditation degree correction value Δ M is calculated；When current time T is later than 12 points and not late When 15, according to formulaMeditation degree correction value Δ M is calculated；When current When quarter T is later than at 15 points and is not later than at 19, according to formulaTo meditation degree correction value Δ M into Row calculates；

In formula (1) and (2), bt1=12；In formula (3), bt2=15；

When being determined in step 3023 to time correcting parameter Δ T, process is as follows:

Step B1, current time integral point value determines: being determined to the integral point value of current time T；The clock circuit (2-6) mentions The current time T of confession is to make for 24 hours, and the integral point value of current time T is denoted as nt, wherein nt be in current time T " when " number Value；

Wherein, nt is integer and nt=0~23；

Step B2, time correcting parameter Δ T is determined: according to nt identified in step B1, and current time T is combined, at this time Time correcting parameter Δ T be determined: when current time T is later than at 6 points and is not later than at 12, according to formulaTime correcting parameter Δ T is calculated；When current time T is later than 12 points and not late When 15, according to formulaTime correcting parameter Δ T is calculated；When current When moment T is later than at 15 points and is not later than at 19, according to formulaTo time correcting parameter Δ T is calculated；

In formula (1) and (2), bt1=12；In formula (3), bt2=15.