CN103584856B - A kind of method by process brain wave identification dynamics nictation - Google Patents

Abstract

A kind of method by process brain wave identification dynamics nictation of the present invention, within each working cycle, first brain wave amplitude x, by iterative formula Y _n=(1-k) Y _n-1+ kx _nand Z _n=(1-k) Z _n-1+ kx _n ²calculate approximate average Y and mean-square value Z, according to formula Var (x)=E (x ²e)-[(x)] ²calculate variance Var (x), then according to formula or formula the amplitude S of x after normalized.And amplitude S is compared with negative sense threshold value with the positive threshold of setting, decision event A or B.If what occur is A event, then thinks and there occurs action nictation, amplitude when dynamics of now blinking is the generation of A event.A kind of method by process brain wave identification dynamics nictation of the present invention, have can fast recognition blink continuously and nictation dynamics, the advantage such as amount of calculation is smaller, method adaptivity is good.

Description

A kind of method by process brain wave identification dynamics nictation

Technical field

The present invention relates to a kind of method by process brain wave identification dynamics nictation.

Background technology

By observing the original amplitude image of brain wave, we find, for anyone, when his health transfixion, action nictation all can cause the fluctuation of forehead EEG ripple, and fluctuation size and nictation dynamics be directly proportional.But because everyone brain wave image has nuance, if process brain wave by setting threshold value, this will cause the product designed to lack universality.If but directly average and variance, huge amount of calculation and computer memory will be caused.

In prior art, use the TGAM module of NeuroSky company can measure original brain wave data and nictation dynamics, but the response speed of this TGAM Module recognition dynamics nictation is very slow, and None-identified continuous print is blinked rapidly.

Summary of the invention

The present invention is for avoiding the weak point that exists in above-mentioned prior art, provides a kind of operand little and can blink by the method for process brain wave identification dynamics nictation continuously by fast recognition, with can fast recognition blink continuously and nictation dynamics.

The present invention be technical solution problem by the following technical solutions.

By a method for process brain wave identification dynamics nictation, it comprises the steps:

Step 1: using ear-lobe as with reference to ground connection, measure the voltage of forehead, observe the oscillogram of brain wave;

Step 2: set a positive threshold and a negative sense threshold value; The amplitude of brain wave is called event A higher than positive threshold, the amplitude of brain wave is called event B lower than negative sense threshold value;

Step 3: within each working cycle of the oscillogram of brain wave, first brain wave amplitude x;

Step 4: carry out iteration by formula (1), obtain the approximation Yn of the average of brain wave amplitude x

Iterative formula (1) is: Y _n=(1-k) Y _n-1+ kx _n(1)

In formula (1), x _nit is the amplitude the n-th moment measuring brain wave; Y _nwhen being n-th iteration, the approximation of the average of amplitude x; K is constant, and n is natural number; If (k is less, Y _nclose to the approximation of real x average, but adaptive speed is slower.Usual k gets 0.005.)

Step 5: carry out iteration by formula (2), obtain the mean-square value Z of brain wave amplitude x _napproximation;

Iterative formula (2) is: Z _n=(1-k) Z _n-1+ kx _n ²(2)

In formula (2), x _nit is the amplitude the n-th moment measuring brain wave; Z _nwhen being n-th iteration, the approximation of the mean-square value of amplitude x; K is constant, and n is natural number; If (k is less, Z _nclose to the approximation of real x mean-square value, but adaptive speed is slower.Usual k gets 0.005.)

Step 6: the variance Var (x) being asked for the approximation of x by formula (3);

Var(x)＝E(x ²)-[E(x)] ²(3)；

Formula (3), x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, E (x ²) mean-square value of brain wave amplitude, Var (x) is the variance of brain wave amplitude;

Step 7: by formula (4), standardization is carried out to x;

$s=\frac{x-E\left(x\right)}{\sqrt{\mathrm{Var}\left(x\right)}}---\left(4\right)$

Formula (4), x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, and Var (x) is the variance of brain wave amplitude; S is the value after x standardization.

The feature of a kind of method by process brain wave identification dynamics nictation of the present invention is also:

In described step 7, adopt formula (5), standardization is carried out to x;

$s^2=\mathrm{sgn}\left(x\right)\frac{{[x-E\left(x\right)]}^2}{\mathrm{Var}\left(x\right)}---\left(5\right);$

Wherein, x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, and Var (x) is the variance of brain wave amplitude; S is the value after x standardization; Function sgn (x) represents: when x is more than or equal to 0, its value is 1; When x is less than or equal to 0, its value is-1.

Compared with the prior art, beneficial effect of the present invention is embodied in:

A kind of method by process brain wave identification dynamics nictation of the present invention, within each working cycle, first brain wave amplitude x, approximate average Y and mean-square value Z is calculated by iterative formula (1) and (2), variance is calculated according to formula (3), then according to the amplitude S of x after formula (4) or formula (5) normalized.And amplitude S is compared with negative sense threshold value with the positive threshold of setting, decision event A or B.If what occur be A event, and the time difference of two event generation time does not exceed preset value, then think and there occurs action nictation, and the maximum rounding S in process individual nictation is dynamics nictation.

A kind of method by process brain wave identification dynamics nictation of the present invention, have can fast recognition blink continuously and nictation dynamics, the advantage such as amount of calculation is smaller, method adaptivity is good.

Accompanying drawing explanation

Fig. 1 is the flow chart of a kind of method by process brain wave identification dynamics nictation of the present invention.

Fig. 2 is the span marked graph of parameter i in Fig. 1.

Fig. 3 is the Matlab analog result figure of a kind of method by process brain wave identification dynamics nictation of the present invention.

Below by way of detailed description of the invention, and the invention will be further described by reference to the accompanying drawings.

Detailed description of the invention

By a method for process brain wave identification dynamics nictation, it is characterized in that, comprise the steps:

Step 1: using ear-lobe as with reference to ground connection, measure the voltage of forehead, observe the oscillogram of brain wave;

Step 2: set a positive threshold and a negative sense threshold value; The amplitude of brain wave is called event A higher than positive threshold, the amplitude of brain wave is called event B lower than negative sense threshold value;

Step 3: within each working cycle of the oscillogram of brain wave, first brain wave amplitude x;

Step 4: carry out iteration by formula (1), obtain the approximation Yn of the average of brain wave amplitude x

Iterative formula (1) is: Y _n=(1-k) Y _n-1+ kx _n(1)

In formula (1), x _nit is the amplitude the n-th moment measuring brain wave; Y _nwhen being n-th iteration, the approximation of the average of amplitude x; K is constant, and n is natural number; If (k is less, Y _nclose to the approximation of real x average, but adaptive speed is slower.Usual k gets 0.005.)

Step 5: carry out iteration by formula (2), obtain the mean-square value Z of brain wave amplitude x _napproximation;

Iterative formula (2) is: Z _n=(1-k) Z _n-1+ kx _n ²(2)

In formula (2), x _nit is the amplitude the n-th moment measuring brain wave; Z _nwhen being n-th iteration, the approximation of the mean-square value of amplitude x; K is constant, and n is natural number; If (k is less, Z _nclose to the approximation of real x mean-square value, but adaptive speed is slower.Usual k gets 0.005.)

Step 6: the variance Var (x) being asked for the approximation of x by formula (3);

Var(x)＝E(x ²)-[E(x)] ²(3)；

Formula (3), x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, E (x ²) mean-square value of brain wave amplitude, Var (x) is the variance of brain wave amplitude;

Step 7: by formula (4), standardization is carried out to x;

$s=\frac{x-E\left(x\right)}{\sqrt{\mathrm{Var}\left(x\right)}}---\left(4\right)$

Formula (4), x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, and Var (x) is the variance of brain wave amplitude; S is the value after x standardization.

It is the flow chart of a specific embodiment of method of the present invention as Fig. 1.During initialization, the value of each parameter is: Y=0; Z=10000; K=0.005; MaxI=100; I=maxI; MaxS=0; Wherein: i is A, B two interval of events number of cycles; As above, maximum wait period constant maxI is 100.Concrete method computational process participates in Fig. 1.

As Fig. 2, it is the span marked graph of parameter i in Fig. 1.For the brain wave waveform of a nictation in Fig. 2, time i can not be long, because if i is long, does not just become continuous print process nictation, therefore need to arrange upper limit maxI.This method, through test, well can identify nictation and dynamics thereof when health is static.But in the process of people's motion, muscle electricity can disturb eeg signal, causes calculated distortion, so also the method for filtering should be adopted, realize in the process of motion, identify nictation and dynamics thereof.

After trying to achieve the variance Var (x) of the approximation of x, by the variance Var (x) of the approximation of x, standardization is carried out to x.In prior art, directly calculate average and the variance of n brain wave data, the average calculating the n moment needs n+1 computing (addition and multiplication) altogether, and variance needs 2n+3 computing.After have employed method of the present invention, computation of mean values and variance are all that needs calculate for 3 times, and this greatly reducing the amount of calculation of whole computational process, is convenient to the identification being carried out dynamics nictation by brain wave.

Experiment finds, the amplitude of brain wave and nictation dynamics be approximate proportional relationship, so dynamics nictation can be represented by the amplitude of brain wave.But due to the waviness amplitude of everyone brain wave and scope neither with, so need algorithm can self adaptation.And the amount of calculation of this algorithm must be little, so that realize process in real time.Method of the present invention, operand little and can fast recognition blink continuously.

Found through experiments, action of can blinking is divided into two stages: close one's eyes, open eyes.Using ear-lobe as with reference to ground connection, measure the voltage of forehead, observe the oscillogram of brain wave.When being used as eye closing action, can produce a pulse identical with the image longitudinal axis positive direction of oscillogram, the amplitude peak of pulse becomes to be similar to proportional relationship with eye closing dynamics.When being used as eye opening action, can produce a pulse contrary with the image longitudinal axis positive direction of oscillogram, the amplitude peak of pulse becomes to be similar to proportional relationship with eye opening eyesight degree.

Set a positive threshold and a negative sense threshold value, the amplitude of brain wave is called event A higher than positive threshold, the amplitude of brain wave is called event B lower than negative sense threshold value.After only having A event, event B occurs, and the difference of two event generation time is less than preset value, just thinks and there occurs action nictation.If there is A event after A event or B event directly occurs, do not think and create action nictation.

Owing to have employed, standardized method is carried out to brain wave amplitude, so positive threshold and negative sense threshold value are all constant.Through experimental verification, positive threshold gets 3, and negative sense threshold value gets-2.

Need to illustrate a bit, owing to being almost close one's eyes firmly full time people deliberately blinks, opening eyes and do not exert oneself, thus should to close one's eyes time dynamics, as the dynamics of nictation.

In described step 7, adopt formula (5), standardization is carried out to x;

$s^2=\mathrm{sgn}\left(x\right)\frac{{[x-E\left(x\right)]}^2}{\mathrm{Var}\left(x\right)}---\left(5\right);$

Wherein, x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, and Var (x) is the variance of brain wave amplitude; S is the value after x standardization; Function sgn (x) represents: when x is more than or equal to 0, its value is 1; When x is less than or equal to 0, its value is-1.

In formula (4), need out radical sign.And very large owing to opening this amount of calculation of radical sign, if be not when good equipment using the method in computing capability, be easy to affect computational speed.And adopt formula (5), then do not open the process that radical sign calculates, computational speed is than very fast.Greatly can reduce amount of calculation like this.When being that amplitude compares, only need by S ²with square comparing of amplitude x.Be the square value of amplitude due to what compare, when comparing numerical values recited, it is also to be noted that amplitude needs to retain original sign simultaneously.

By matlab, method is simulated.Following Fig. 3 of result (eeg signal is all by TGAM module acquires) of simulation, value k gets 0.00015 here.Black zone in figure represents brain wave figure, and the some representative of brain wave figure middle white is carved at this moment and recognized action nictation.The numeral on the straight line of the some correspondence of white and straight line side, represents dynamics nictation measured in this moment.

The computational process of the method in each working cycle is as follows: within each working cycle, first brain wave amplitude x, approximate average Y and mean-square value Z is calculated by iterative formula (1) and (2), variance is calculated according to formula (3), then according to the amplitude S of x after formula (4) or formula (5) normalized.And S is compared with negative sense threshold value with the positive threshold of setting, decision event A or B.

Judge to there occurs event A or B by S, if there occurs the B time, when seeing last generation event, occur what, if what occur is A event, and the difference of two event generation time is less than preset value, then thinks and there occurs action nictation, the maximum rounding S in process nictation is dynamics nictation.

Method of the present invention, by measuring forehead EEG ripple, identifies the method for dynamics nictation.The method entered test, had adaptivity, and method is simple, was convenient to realize.Through dynamics nictation that method process obtains, have enough precision.

Method by process brain wave identification dynamics nictation of the present invention, comprise the standardized method of digitized brain wave amplitude data and the data how after cleanup standard, there is provided that a kind of real-time operand is little, fast operation can identify continuously nictation and nictation dynamics method, can distinguish intentional nictation and unconscious nictation.

Method of the present invention, through test, well can identify nictation and dynamics thereof when health is static.

Claims (2)

1., by a method for process brain wave identification dynamics nictation, it is characterized in that, comprise the steps:

Step 1: using ear-lobe as with reference to ground connection, measure the voltage of forehead, observe the oscillogram of brain wave;

Step 2: set a positive threshold and a negative sense threshold value; The amplitude of brain wave is called event A higher than positive threshold, the amplitude of brain wave is called event B lower than negative sense threshold value;

Step 3: within each working cycle of the oscillogram of brain wave, first brain wave amplitude x;

Step 4: carry out iteration by formula (1), obtain the approximation Yn of the average of brain wave amplitude x

Iterative formula (1) is: Y _n=(1-k) Y _n-1+ kx _n(1)

In formula (1), x _nit is the amplitude the n-th moment measuring brain wave; Y _nwhen being n-th iteration, the approximation of the average of amplitude x; K is constant, and n is natural number;

Step 5: carry out iteration by formula (2), obtain the mean-square value Z of brain wave amplitude x _napproximation;

Iterative formula (2) is: Z _n=(1-k) Z _n-1+ kx _n ²(2)

In formula (2), x _nit is the amplitude the n-th moment measuring brain wave; Z _nwhen being n-th iteration, the approximation of the mean-square value of amplitude x; K is constant, and n is natural number;

Step 6: the variance Var (x) being asked for the approximation of x by formula (3);

Var(x)＝E(x ²)-[E(x)] ²(3)；

Formula (3), x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, E (x ²) mean-square value of brain wave amplitude, Var (x) is the variance of brain wave amplitude;

Step 7: by formula (4), standardization is carried out to x;

$s=\frac{x-E\left(x\right)}{\sqrt{\mathrm{Var}\left(x\right)}}---\left(4\right)$

Formula (4), x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, and Var (x) is the variance of brain wave amplitude; S is the value after x standardization; Amplitude S is compared with negative sense threshold value with the positive threshold of setting, decision event A or B; If what occur be A event, and the time difference of two event generation time does not exceed preset value, then think and there occurs action nictation, and the maximum rounding S in process individual nictation is dynamics nictation.

2. a kind of method by process brain wave identification dynamics nictation according to claim 1, is characterized in that, in described step 7, adopt formula (5), carry out standardization to x;

$s^2=\mathrm{sgn}\left(x\right)\frac{{[x-E\left(x\right)]}^2}{\mathrm{Var}\left(x\right)}---\left(5\right);$

Wherein, x is the amplitude of brain wave, and E (x) is the average of brain wave amplitude, and Var (x) is the variance of brain wave amplitude; S is the value after x standardization; Function sgn (x) represents: when x is more than or equal to 0, its value is 1; When x is less than or equal to 0, its value is-1.