CN111973179B - Brain wave signal processing method, brain wave signal processing device, electronic device, and storage medium - Google Patents
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Abstract
The application discloses a brain wave signal processing method, a brain wave signal processing device, electronic equipment and a storage medium, wherein the brain wave signal processing method comprises the following steps: collecting brain wave signals; preprocessing the brain wave signals to obtain preprocessed brain wave signals; acquiring a plurality of frequency band signals of the brain wave signals after preprocessing; normalizing the plurality of frequency band signals to obtain a plurality of frequency band signals after normalization; and integrating the plurality of frequency band signals after the normalization processing into a time signal. By means of the technical scheme, the signal to noise ratio of epileptiform discharge waveforms such as spike waves in brain wave signals can be improved, the difference between the epileptiform discharge waveforms and background signal waveforms is highlighted, and the difficulty of visual detection and manual marking of abnormal brain wave waveforms such as spike waves by neurologists can be reduced.
Description
Technical Field
The present application relates to the field of signal processing technologies, and in particular, to a brain wave signal processing method and apparatus, an electronic device, and a storage medium.
Background
At present, brain science is the leading science of people's key attention, electroencephalogram is a sensitive index for evaluating brain functional state, can be used for positioning paroxysmal brain dysfunction such as epilepsy and evaluating the range and degree of epileptic brain function damage, and is widely applied to the research of central nervous system diseases and psychogenic diseases.
In the analysis and interpretation of the electroencephalogram, the electroencephalogram is manually and visually analyzed by doctors mainly depending on the experience of neurologists, in the analysis process, the doctors need to firstly overview the whole situation, preliminarily judge the main parts of intervals and attack periods, hide irrelevant parts according to the conditions, amplify the key local leads, and carefully analyze the time-space evolution relation of frequency, voltage and waveforms. In addition, parameters such as bandwidth, sensitivity, screen display time and the like are adjusted at any time according to needs, relevant pathological information is obtained, the time and the position of the brain wave are marked, and an electroencephalogram report is generated.
The technical scheme at least has the following problems:
the efficiency of completely relying on human eye recognition is low, and the task is heavy;
under the condition that abnormal brain waveforms such as spike waves and the like exist in brain wave signals, due to the fact that the sensitivity of human eyes to the abnormal brain waveforms is poor, the visual inspection analysis difficulty is high, and omission easily occurs;
the synchronous pattern characteristics of brain wave signals are very important information, but it is difficult to observe the synchronicity of discharge waveforms among multiple channels.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a brain wave signal processing method, a brain wave signal processing device, electronic equipment and a storage medium, and aims to solve the technical problem that visual analysis of abnormal brain waveforms in related technologies is difficult.
The embodiment of the disclosure provides a brain wave signal processing method, which includes: collecting brain wave signals; preprocessing the brain wave signals to obtain preprocessed brain wave signals; acquiring a plurality of frequency band signals of the brain wave signals after preprocessing; normalizing the plurality of frequency band signals to obtain a plurality of frequency band signals after normalization; and integrating the plurality of frequency band signals after the normalization processing into a time signal.
In some embodiments, the preprocessing of the brain wave signals includes: removing the signals of the non-target electrode channels in the brain wave signals to obtain the signals of the target electrode channels; calculating the average value of signals of all target electrode channels; performing re-reference processing by taking the mean value as reference data; removing the base drift signal and the power frequency signal in the brain wave signal obtained by re-referencing to obtain a brain wave signal after removal; filtering the brain wave signals subjected to the removal processing to obtain brain wave signals within a target frequency band range; and resampling the brain wave signals in the target frequency band range according to a preset sampling rate to obtain the preprocessed brain wave signals.
In some embodiments, normalizing the plurality of frequency band signals comprises:
where X' denotes the current band signal after the normalization process, X denotes the current band signal before the normalization process, mean (X) denotes an average value of the plurality of band signals, max (X) denotes a maximum band signal of the plurality of band signals, and min (X) denotes a minimum band signal of the plurality of band signals.
In some embodiments, integrating the normalized multiple frequency band signals into one time signal includes:
wherein, TsignalRepresenting the integrated time signal; n is a radical ofbandIndicates the number of frequency bands, Xi' denotes the i-th band signal after the normalization process.
In some embodiments, after integrating the normalized multiple frequency band signals into one time signal, the method further comprises: and mapping the time signals to obtain a pseudo-color image, wherein the pseudo-color image comprises the synchronous characteristics of abnormal discharge signals in the brain wave signals among different electrode channels.
In some embodiments, the brain wave signals include EEG signals, and mapping the time signals into a pseudo-color image includes: determining an RGB intensity value corresponding to each pixel value of the time signal; and combining the RGB intensity values corresponding to each pixel value to obtain a pseudo-color image.
In some embodiments, the brain wave signals include SEEG signals, and the time signals are mapped to obtain pseudo-color images, including: extracting the energy of the time signal; performing Hilbert transform processing on the energy to obtain an envelope of the energy; the envelope of the energy is displayed in pseudo-color.
The disclosed embodiment provides a brain wave signal processing device, including: a collecting unit configured to collect brain wave signals; the preprocessing unit is configured to preprocess the brain wave signals to obtain the preprocessed brain wave signals; an acquisition unit configured to acquire a plurality of frequency band signals of the brain wave signals after the preprocessing; the normalization unit is configured to perform normalization processing on the plurality of frequency band signals to obtain a plurality of frequency band signals after normalization processing; an integration unit configured to integrate the plurality of frequency band signals after the normalization processing into one time signal.
The disclosed embodiments also provide an electronic device including a processor and a memory storing program instructions, the processor being configured to execute the above-described brain wave signal processing method when executing the program instructions.
The disclosed embodiments also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to perform the above-described brain wave signal processing method.
The brain wave signal processing method, the brain wave signal processing device, the electronic equipment and the storage medium provided by the embodiment of the disclosure can achieve the following technical effects:
according to the embodiment of the disclosure, the brain wave signals are collected, the brain wave signals are preprocessed to obtain the preprocessed brain wave signals, the multiple frequency band signals of the preprocessed brain wave signals are obtained, the multiple frequency band signals are normalized to obtain the multiple frequency band signals after normalization, and the multiple frequency band signals after normalization are integrated into one time signal, so that the signal-to-noise ratio of the discharge waveforms of epilepsy such as spike waves and the like in the brain wave signals is improved, the difference between the discharge waveforms and the background signal waveform is highlighted, and the visual detection and manual marking difficulty of doctors on abnormal brain waveforms such as spike waves and the like can be reduced. Moreover, by mapping the electroencephalogram signals into the pseudo-color images, doctors can observe the synchronous characteristics of abnormal electroencephalograms among the channels conveniently and intuitively, and the efficiency and quality of electroencephalogram interpretation analysis are improved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
At least one embodiment is illustrated by the accompanying drawings, which correspond to the accompanying drawings, and which do not form a limitation on the embodiment, wherein elements having the same reference numeral designations are shown as similar elements, and which are not to scale, and wherein:
fig. 1 is a schematic flow chart of a brain wave signal processing method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a epileptiform discharge waveform in an EEG signal provided by an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an epileptiform discharge waveform following frequency normalization in an EEG signal provided by embodiments of the present disclosure;
fig. 4 is a schematic diagram of a epileptiform discharge waveform in a segg signal provided by an embodiment of the present disclosure;
fig. 5 is a schematic diagram of an energy envelope after frequency normalization in a SEEG signal provided by an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a brain wave signal processing apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, at least one embodiment may be practiced without these specific details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
In the following examples, epilepsy is taken as an example to illustrate the method of the present invention, but the method of the present invention is not limited thereto, and can be used for signal processing of other types of electroencephalograms.
According to the data of the world health organization, 5000 ten thousand epileptic patients exist in the world at present, and the epilepsy becomes a common nervous system disease. The statistical data of the Chinese antiepileptic society shows that 900 ten thousand patients with epilepsy exist in China at present. Electroencephalograms include scalp electroencephalograms (EEG) and intracranial electroencephalograms (SEEG). The electroencephalogram abnormality of epilepsy can be divided into inter-attack periods and attack periods, and abnormal waveforms in the inter-attack periods mainly include spike waves, sharp waves, spike-slow waves, slow wave dispersion or various rhythms. Spike waves are waveform changes in electroencephalograms due to abnormal discharge of cerebral cortical nerve cells, and are one of the typical characteristics of epileptiform discharges. Electroencephalography during a seizure may see an abnormal seizure electroencephalogram event with an evolutionary process from beginning to end.
Referring to fig. 1, the disclosed embodiment provides a brain wave signal processing method, it should be understood that the brain wave signal processing method shown in fig. 1 may be performed by a brain wave signal processing apparatus, which may correspond to the brain wave signal processing apparatus shown in fig. 6 hereinafter, which may be various devices capable of performing the method, such as a personal computer, a server, a network device, or the like, for example, and the disclosed embodiment is not limited thereto. The method comprises the following steps:
and S101, collecting brain wave signals.
Optionally, the brain wave signal includes at least one of an EEG signal, a segg signal.
Optionally, the brain wave signals are collected by an electroencephalogram amplifier according to clinical standards, the sampling rate of the EEG signals can be 500-1000 Hz, the channel number can be 16-32, and the collection time of the EEG signals is 2 hours or more. For SEEG signals, the sampling rate may be 2000Hz, the number of channels exceeds 100 leads, and the acquisition time may be as long as 1 day to a week.
Optionally, the sampling rate, the number of channels, the acquisition time, and the like may be set according to actual requirements.
And S102, preprocessing the brain wave signals to obtain the preprocessed brain wave signals.
Optionally, the preprocessing the brain wave signal to obtain a preprocessed brain wave signal includes: removing signals of non-target electrode channels in the brain wave signals to obtain signals of the target electrode channels, then calculating the mean value of all signals of the target electrode channels, then performing re-reference processing by taking the mean value as reference data, then removing a base drift signal and a power frequency signal in the brain wave signals obtained by re-reference to obtain the brain wave signals after removal processing, then performing filtering processing on the brain wave signals after removal processing to obtain the brain wave signals within a target frequency band range, and then performing resampling processing on the brain wave signals within the target frequency band range according to a preset sampling rate to obtain the brain wave signals after preprocessing.
Optionally, in the EEG signal, some channel information that is not needed in the later period may be recorded, such as the signal of the electrooculogram channel, bilateral papillary dots, etc., which may be removed first without being included in the subsequent analysis. In the SEEG signal, the signal of the electrocardiographic channel and the like can be removed. In addition, in order to reduce the interference of high frequency components by noise, two electrode channels near the skull bone can be removed. That is, the signals of the non-target electrode channels (for example, electrode channels that are not needed) in the brain wave signals may be removed to obtain the signals of the target electrode channels.
Alternatively, a whole brain average reference is used for both EEG and SEEG signals, i.e. the mean of the signals of all electrode channels is used as reference data. The reference data is subtracted from each value of the EEG or segg signal to obtain the EEG or segg signal after re-referencing. That is, the average value of the signals of all the target electrode channels may be calculated, and then re-reference processing is performed with the average value as reference data.
Optionally, in order to eliminate the influence caused by data drift, high-pass filtering of 0.1Hz may be performed on the EEG signal after re-referencing, so as to obtain the EEG signal after removing the base drift. For the SEEG signal, high-pass filtering of 0.1Hz is carried out similarly, and the SEEG signal after the base drift is removed is obtained. That is, the base-drift signal in the re-referenced electroencephalogram signal can be removed to obtain an electroencephalogram signal after the removal process.
Optionally, a 50Hz notch filter can be adopted to remove the power frequency signal, so as to obtain an EEG signal after the power frequency signal is removed; for SEEG signals, in addition to 50Hz, power frequency signals of frequency multiplication components of 50Hz are removed, and SEEG signals after power frequency removal are obtained. That is, the electroencephalogram signal after the removal processing can be obtained by performing the removal processing on the power frequency signal in the electroencephalogram signal obtained by the re-referencing.
Although the above description is given by taking 50Hz or 50Hz frequency multiplication as an example, it should be understood by those skilled in the art that the specific value of the power frequency signal can be set according to actual requirements, and the embodiments of the present disclosure are not limited thereto.
Optionally, a common frequency band range of the epileptiform discharge waveform in the EEG signal is below 100Hz, and a 100Hz low-pass filter can be designed to obtain the filtered EEG signal; for SEEG signals, the frequency range of relevant waveforms (such as high-frequency oscillation HFO, spike waves and the like) of epileptic abnormality is mainly 80-250 Hz, and a band-pass filter of 80-250 Hz can be designed to obtain the SEEG signals after filtering. That is, the brain wave signal after the removal processing may be subjected to filtering processing to obtain a brain wave signal within the target frequency band.
Although the above description is given by taking a 100Hz low pass filter and a 80-250 Hz band pass filter as examples, it should be understood by those skilled in the art that the specific frequency value (or frequency range) of the low pass filter and the frequency range (or specific frequency value) of the band pass filter can be set according to actual requirements, and the embodiments of the present disclosure are not limited thereto.
Alternatively, to reduce the amount of data and increase the speed of the calculation, the EEG signal may be downsampled. In order to reduce the data volume and ensure that the EEG signal is not distorted, namely, the sampling law is satisfied, the sampling rate can be set to be 200Hz, and the EEG signal after resampling is obtained; for the SEEG signal, resampling with the sampling rate of 500Hz can be carried out, and the SEEG signal after resampling is obtained. That is, the brain wave signals within the target frequency band are resampled at a preset sampling rate, and the brain wave signals after being preprocessed are obtained.
Although the above description is made by taking the sampling rate of a specific frequency as an example, it should be understood by those skilled in the art that the specific frequency of the sampling rate can be set according to actual requirements.
Step S103, acquiring a plurality of frequency band signals of the brain wave signal after the preprocessing.
Alternatively, a third-order butterworth band pass filter may be used to extract the respective frequency band signals of the EEG signal and the SEEG signal.
Optionally, 6 different frequency band ranges can be set for the EEG signal, and the frequency band ranges are 1-2 Hz, 2-4 Hz, 4-8 Hz, 8-16 Hz, 16-32 Hz, and 32-64 Hz respectively. The EEG signals of these 6 different frequency bands were obtained separately by band-pass filters.
Optionally, for the SEEG signal, a frequency band can be set in a frequency band range of 80-250 Hz every 20Hz, and a frequency band signal group of the SEEG signal is obtained, wherein the frequency band signal group comprises a plurality of frequency band signals.
Step S104, performing normalization processing on the plurality of frequency band signals to obtain a plurality of frequency band signals after normalization processing.
Alternatively, after obtaining the EEG signals or segg signals of different frequency band ranges, normalization processing may be performed on the frequency band signals to obtain normalized EEG signals or segg signals of different frequency band ranges. Wherein, the normalization formula is as follows:
where X' denotes the current band signal after the normalization process, X denotes the current band signal before the normalization process, mean (X) denotes an average value of the plurality of band signals, max (X) denotes a maximum band signal of the plurality of band signals, and min (X) denotes a minimum band signal of the plurality of band signals.
Step S105 integrates the plurality of frequency band signals after normalization processing into one time signal.
Optionally, the normalized EEG signal is integrated into a time signal comprising the normalized EEG signal for 6 frequency band ranges, whereby the frequency band signal with higher frequencies is highlighted.
Referring to fig. 2, a schematic diagram of a epileptiform discharge waveform in an EEG signal; see fig. 3, which is a schematic of the epileptiform discharge waveform after frequency normalization in an EEG signal. At the location of the box marks shown in fig. 3, the EEG signal waveforms after frequency normalization of the box marked EEG signals shown in fig. 2 are shown.
Fig. 4 is a schematic diagram of a epileptiform discharge waveform in the segg signal; referring to fig. 5, a diagram of an energy envelope after frequency normalization in the SEEG signal is shown. And for the SEEG signal, integrating the normalized signal within the normalized frequency band range of 20Hz within the range of 80-250 Hz into a time signal by adopting the same method. Wherein, the signal integration formula is as follows:
wherein, TsignalRepresenting the integrated time signal; n is a radical ofbandIndicates the number of frequency bands, Xi' denotes the i-th band signal after the normalization process.
And step S106, mapping the time signals to obtain a pseudo-color image, wherein the pseudo-color image comprises the synchronous characteristics of abnormal discharge signals in the brain wave signals among different electrode channels.
Optionally, in the case that the brain wave signals include EEG signals, mapping the time signals into a pseudo color image includes: determining an RGB intensity value corresponding to each pixel value of the time signal; and combining the RGB intensity values corresponding to each pixel value to obtain a pseudo-color image.
The place of the vertical strip in the pseudo-color image represents the synchronous occurrence of the abnormal epileptic discharge waveform such as spike wave and the like among different electrode channels. In addition, locations where the EEG signal waveform amplitude is large can be mapped as a "color bar" in the pseudo-color map.
Alternatively, in the case where the brain wave signal includes a seg signal, mapping the time signal into a pseudo-color image includes: extracting the energy of the time signal; performing Hilbert transform processing on the energy to obtain an envelope of the energy; the envelope of the energy is displayed in pseudo-color. Wherein, the envelope calculation formula of the energy is as follows:
wherein S isenergy(t) represents the energy of the SEEG signal after the normalized frequency band signal integration; t represents a time sampling point; enveenergyAn envelope representing the energy of the SEEG signal; denotes convolution operation.
According to the embodiment of the disclosure, the signal to noise ratio of the epileptiform discharge waveforms such as spike waves in brain wave signals is improved by performing normalization processing and pseudo-color display on the frequency of EEG signals or SEEG signals, the difference between the waveform and background signal waveform is highlighted, and the visual detection and manual marking difficulty of neurologists on abnormal brain wave waveforms such as spike waves can be reduced; by mapping the brain wave signals into the pseudo-color images, the difficulty of neurologists in observing multichannel epileptic abnormal discharge can be reduced, and the efficiency and the quality of electroencephalogram interpretation analysis are improved.
It should be understood that the above-described brain wave signal processing method is only exemplary, and those skilled in the art may make various modifications, adaptations, or variations according to the above-described method within the scope of the present application.
Referring to fig. 6, fig. 6 illustrates a block diagram of a brain wave signal processing apparatus 600 according to an embodiment of the present disclosure, it should be understood that the brain wave signal processing apparatus 600 corresponds to the above-described method embodiment and is capable of performing the steps related to the above-described method embodiment, and the detailed description of the brain wave signal processing apparatus 600 may be referred to in the above description, and is omitted here as appropriate to avoid redundancy. The brain wave signal processing apparatus 600 includes at least one software functional module that can be stored in a memory in the form of software or firmware (firmware) or is solidified in an Operating System (OS) of the brain wave signal processing apparatus 600. Specifically, the brain wave signal processing apparatus 600 includes:
an acquisition unit 610 configured to acquire brain wave signals; a preprocessing unit 620 configured to preprocess the brain wave signals, resulting in preprocessed brain wave signals; an acquisition unit 630 configured to acquire a plurality of frequency band signals of the brain wave signals after the preprocessing; a normalization unit 640 configured to perform normalization processing on the plurality of frequency band signals, so as to obtain a plurality of frequency band signals after normalization processing; an integrating unit 650 configured to integrate the plurality of frequency band signals after the normalization processing into one time signal.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method, and will not be described in too much detail herein.
An embodiment of the present disclosure provides an electronic device, a structure of which is shown in fig. 7, including:
a processor (processor)701 and a memory (memory)702, and may further include a Communication Interface 703 and a bus 704. The processor 701, the communication interface 703 and the memory 702 may communicate with each other through a bus 704. Communication interface 703 may be used for the transfer of information. The processor 701 may call logic instructions in the memory 702 to perform the brain wave signal processing method of the above-described embodiment.
Furthermore, the logic instructions in the memory 702 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product.
The memory 702 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 701 executes functional applications and data processing by executing program instructions/modules stored in the memory 702, that is, implements the brain wave signal processing method in the above-described method embodiment.
The memory 702 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. In addition, the memory 702 may include high speed random access memory, and may also include non-volatile memory.
The disclosed embodiments provide a computer-readable storage medium storing computer-executable instructions configured to perform the above-described brain wave signal processing method.
The disclosed embodiments provide a computer program product including a computer program stored on a computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the above-described brain wave signal processing method.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The computer-readable storage medium and the computer program product provided by the embodiments of the present disclosure can reduce the difficulty of analyzing electroencephalogram signals, shorten the signal acquisition time, and improve the efficiency and accuracy of analyzing electroencephalogram signals.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes at least one instruction to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the disclosed embodiments includes the full ambit of the claims, as well as all available equivalents of the claims. As used in this application, although the terms "first," "second," etc. may be used in this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, unless the meaning of the description changes, so long as all occurrences of the "first element" are renamed consistently and all occurrences of the "second element" are renamed consistently. The first and second elements are both elements, but may not be the same element. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It is clear to those skilled in the art that, for convenience and brevity of description, the working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit may be merely a division of a logical function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises at least one executable instruction for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (7)
1. A brain wave signal processing method is characterized by comprising the following steps:
collecting brain wave signals;
preprocessing the brain wave signals to obtain preprocessed brain wave signals;
acquiring a plurality of frequency band signals of the brain wave signals after the preprocessing;
normalizing the plurality of frequency band signals to obtain a plurality of frequency band signals after normalization;
and integrating the plurality of frequency band signals after the normalization processing into a time signal.
2. The method according to claim 1, wherein the preprocessing the brain wave signals comprises:
removing the signals of the non-target electrode channels in the brain wave signals to obtain the signals of the target electrode channels;
calculating the average value of signals of all target electrode channels;
performing re-reference processing by taking the mean value as reference data;
removing the base drift signal and the power frequency signal in the brain wave signal obtained by re-referencing to obtain a brain wave signal after removal;
filtering the brain wave signals subjected to the removal processing to obtain brain wave signals within a target frequency band range;
and resampling the brain wave signals within the target frequency band range according to a preset sampling rate to obtain the preprocessed brain wave signals.
3. The method of claim 1, wherein the normalizing the plurality of frequency band signals comprises:
where X' denotes the current band signal after the normalization process, X denotes the current band signal before the normalization process, mean (X) denotes an average value of the plurality of band signals, max (X) denotes a maximum band signal of the plurality of band signals, and min (X) denotes a minimum band signal of the plurality of band signals.
4. The method according to claim 1, wherein the integrating the normalized frequency band signals into a time signal comprises:
wherein, TsignalRepresenting the integrated time signal; n is a radical ofbandDenotes the number of bands, X'iIndicating the normalized ith frequency band signal.
5. A brain wave signal processing apparatus, comprising:
a collecting unit configured to collect brain wave signals;
a preprocessing unit configured to preprocess the brain wave signals to obtain preprocessed brain wave signals;
an acquisition unit configured to acquire a plurality of frequency band signals of the brain wave signals after the preprocessing;
a normalization unit configured to perform normalization processing on the plurality of frequency band signals to obtain a plurality of frequency band signals after normalization processing;
an integration unit configured to integrate the plurality of frequency band signals after the normalization processing into one time signal.
6. An electronic device comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the brain wave signal processing method according to any one of claims 1 to 4 when executing the program instructions.
7. A computer-readable storage medium, characterized in that a computer program is stored thereon, which when executed by a processor, implements the brain wave signal processing method according to any one of claims 1 to 4.
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| CN202010862446.0A CN111973179B (en) | 2020-08-25 | 2020-08-25 | Brain wave signal processing method, brain wave signal processing device, electronic device, and storage medium |
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