CN102436302A - Method for brain-computer interface based on amplitude modulated visual evoked potential - Google Patents

Abstract

The invention discloses a method for a brain-computer interface based on amplitude modulated visual evoked potential. The method comprises the following steps of: firstly, connecting an electroencephalogram signal acquisition system; optimally selecting the most-sensitive frequency of amplitude frequency response of a tested person as carrier frequency; maximizing a signal-noise ratio of a response signal; secondly, providing a selection range of modulation wave frequency, designing an AMVEP (Amplitude Modulated Visual Evoked Potential) normal form and generating a stimulation sequence of an amplitude modulated visual evoked potential brain-computer interface normal form; and finally, realizing the identification of single signal or a small amount of signals of the normal form AMVEP. The invention provides the normal form based on the amplitude modulated visual evoked potential and a method for analysis and characteristic extraction of a modulation signal corresponding to the normal form AMVEP. The identification of single signal or a small amount of signals of the normal form AMVEP based on the amplitude modulated visual evoked potential is realized; and the method disclosed by the invention has the advantages of simpleness in operation, fewer electrodes and more target numbers.

Description

Based on which amplitude modulation VEP brain-computer interface method

Technical field

The present invention relates to the brain-computer interface technical field, be specifically related to based on which amplitude modulation VEP brain-computer interface method.

Background technology

Brain-computer interface is based on EEG signals and realizes that brain and computing machine or other electronic equipments directly exchange the system of communication and control.Brain-computer interface (Brain-Computer Interface; Be called for short BCI) as man machine interface (Human-computer interface; Be called for short HCI) in a kind of; Can directly convert the information that brain sends into and can drive external unit order, and replace people's musculatures such as limbs to realize that the people exchanges and to the control of external environment condition, owing to do not rely on the brain output channel of routine with extraneous; The brain of behaving has been opened up a brand-new approach that carries out information interchange and control with the external world, and the feasible idea of utilizing people's brain signal directly to control external unit becomes possibility.In recent years, brain-computer interface (BCI) technical development is very fast, embodies significant values in fields such as biomedicine, virtual reality, Entertainment, rehabilitation project and space flight, military affairs.

Stable state vision inducting electricity (Steady State Visually Evoked Potential; SSVEP) be that the brain vision system continues the periodically response of visual stimulus to the outside; It is input signal commonly used in the brain-computer interface BCI system; Compare signals such as P300, incident related synchronization, spontaneous brain electricity; Stable state vision inducting current potential (SSVEP) shows following research potential and potential practical value owing to have outstanding advantages such as the controlled target number is many, rate of information transmission is high and antijamming capability is strong, the training time is short, recording electrode is few, simple to operate, so becomes the research normal form that has wide application prospect and using value in the practical BCI system.

In present SSVEP-BCI system applies; SSVEP mainly utilizes the low frequency region below the 30Hz; Adopt the cursor flicker stimulation mode of a goal task of a frequency representation; Because the restriction of frequency field, frequency resolution and the response amplitude of target performance in order to guarantee the recognition correct rate of goal task, makes that the task object number that can appear is limited.Simultaneously, traditional SSVEP identification efficient is low, needs repeatedly stimulus signal to add up, and has had a strong impact on the real-time identification ability.Therefore, the target numbers that increase can appear, the rate of information transmission that promotes the SSVEP technology becomes the key problem of present SSVEP-BCI system.

Summary of the invention

In order to overcome the shortcoming of above-mentioned prior art; The invention provides based on which amplitude modulation VEP brain-computer interface method; Normal form (Amplitude Modulated Visual Evoked Potential based on the which amplitude modulation VEP has been proposed; Be called for short AMVEP); And modulation signal analysis and the feature extracting method adapt with it proposed, realize having advantage simple to operate, that electrode number is few and number of targets is many based on the AMVEP single or few the signal identification of the normal form of which amplitude modulation VEP.

In order to achieve the above object, the technical scheme taked of the present invention is:

Based on which amplitude modulation VEP brain-computer interface method, may further comprise the steps:

Step 1; The Oz position of sound production potential electrode A in the occipital region of subject's head D; At the one-sided ear-lobe position of sound production reference electrode B of subject's head D, at the frons Fpz of subject's head D position of sound production ground electrode C, the output terminal of potential electrode A inserts the first input end E1 of eeg amplifier E; The output terminal of reference electrode B inserts the second input end E2 of eeg amplifier E; The output terminal of ground electrode C inserts the 3rd input end E3 of eeg amplifier E, and the output terminal of eeg amplifier E links to each other with the input end of computing machine F, and the output terminal of computing machine F and computer screen G are connected to form the eeg signal acquisition system;

Step 2; Stimulate normal form to be presented in face of the experimenter the denotable unifrequency of computer screen G through computer screen G; Subject's head D distance calculation screen G is 50～100 centimetres; According to the experimenter different unifrequencys stimulate down bring out potential response amplitude susceptibility rule, the responsive stimuli responsive frequency of optimized choice is as the carrier frequency of modulation VEP stimulation normal form, the optimized choice principle is: according to experimenter's difference; Make the amplitude-frequency response figure of the optional frequency of this experimenter; From amplitude-frequency response figure, select the pairing frequency values of amplitude maximal value as carrier frequency, promptly select the most responsive frequency of experimenter's amplitude-frequency response as carrier frequency, the signal to noise ratio (S/N ratio) of maximization response signal;

Step 3; According to the selected carrier frequency of step 2; And influence the factor that the modulating wave modulating frequency is selected, and the range of choice of modulating frequency is proposed, comprise screen refresh rate and spectral resolution owing to influence the factor of modulating wave; So carrier frequency must meet sampling thheorem to the modulating wave modulating frequency, the method that the modulating wave modulating frequency is selected specifically comprises following content:

Amplitude modulated signal: y (t)=C+C*sin (2* π * f _m* Δ t _m-pi/2) (1)

Sampling thheorem: $\frac{f_{\mathrm{Ms}}}{f_m}\≥2.56---\left(2\right)$

The transformation relation of parameter is following in the amplitude modulated signal computing formula:

f _Ms→ f ₀, that is: ${\mathrm{\&Delta;\; t}}_m\&RightArrow;{\mathrm{\&Delta;\; <figure-callout\; id="0"\; label="t"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">t</figure-callout>}}_0=\frac{1}{{\mathrm{<figure-callout\; id="0"\; label="f"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">f</figure-callout>}}_0}=\frac{n}{f_r}---\left(3\right)$

${\mathrm{\&Delta;<figure-callout\; id="0"\; label="t"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">t</figure-callout>}}_0={\mathrm{\&Delta;t}}_m*\mathrm{\&lambda;}\&DoubleRightArrow;\frac{1}{\mathrm{\&lambda;}}=\frac{{\mathrm{\&Delta;t}}_m}{{\mathrm{\&Delta;<figure-callout\; id="0"\; label="t"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">t</figure-callout>}}_0}=\frac{1}{f_{\mathrm{ms}}}/\frac{n}{f_r}=\frac{f_r}{f_{\mathrm{ms}}*n}---\left(4\right)$

Learn that by above formula (1)～(4) derivation final frequency of modulated wave is:

$x=\frac{f_m}{\mathrm{\&lambda;}}=\frac{f_m*f_r}{f_{\mathrm{ms}}*n}\&DoubleRightArrow;\frac{f_{\mathrm{ms}}}{f_m}=\frac{f_r}{n*x}\&GreaterEqual;2.56\&DoubleRightArrow;x\&le;\frac{f_r}{n*2.56}$

Wherein:

C: constant

f _Ms: the SF of amplitude modulated signal

f _m: the frequency of amplitude modulated signal

f ₀: carrier frequency

: the SI of amplitude modulated signal

X: modulation wave frequency

N: the frame number of the required screen-refresh of expression one-period

f _r: screen refresh rate

By above shift onto process learn frequency of modulated wave scope should for:

$0\&le;x\&le;\frac{f_r}{n*2.56}=\frac{{\mathrm{<figure-callout\; id="0"\; label="f"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">f</figure-callout>}}_0}{2.56};$

Step 4; According to the selected carrier frequency of step 2 optimization principles; And the range of choice of frequency of modulated wave that step 3 proposes, design AMVEP normal form, the stimulus sequence of generation which amplitude modulation VEP brain-computer interface normal form; Be presented in face of the experimenter through computer screen G, the computing formula of stimulus sequence that produces the AMVEP normal form is following:

$y_{\mathrm{AMVEP}}\left(t\right)=C+C*\sin (2*\mathrm{\&pi;}*\frac{n*x}{f_r}-\mathrm{pi}/2);$

Wherein, C is a constant, and x is by being designed frequency of modulated wave, and knows according to step 3:

$x\&le;\frac{f_r}{n*2.56};$

Step 5; Confirm modulating-coding SSVEP normal form through step 2～3 earlier; Through step 4 it is presented in face of the experimenter again, finally obtains experimenter's AMVEP response signal, propose the modulation signal analysis and the feature extracting method that adapt with it through the described eeg signal acquisition of step 1 system; Realize single or few the signal identification of AMVEP, specifically may further comprise the steps:

◆ the AMVEP response eeg data to the different stimulated sequence that collects carries out bandpass filtering;

◆ adopt the Hilbert demodulation method that filtered data are carried out amplitude demodulation;

◆ the data after adopting the FFT method to amplitude demodulation are asked its spectrum maximum;

◆ according to spectrum results, obtain the maximum value frequency value corresponding in the frequency spectrum, be the frequency of modulated wave value.

The present invention is directed to present BCI system and have the problem that system stability is not high, rate of information transmission is low that target numbers is few, stimulation time is long, experimental paradigm simply causes based on SSVEP; Proposed to bring out current potential brain-computer interface normal form and the feature extraction demodulation method that adapts with it based on modulation; Realize single or few the signal identification of AMVEP; For realize to make up with efficient and harmless be final goal; Have brain-computer interface technology simple to operate, that electrode number is few, number of targets is many and opened up new thinking, shown following superiority:

1, modulation VEP brain-computer interface normal form

Propose modulation VEP brain-computer interface normal form, increased the optional number of targets of SSVEP greatly, and utilized the strong advantage of responsive carrier frequency antijamming capability, improved the signal to noise ratio (S/N ratio) of response signal.

2, which amplitude modulation is brought out the feature extracting method of potential response

Propose features of response based on AMVEP and propose individual features extraction signal Processing scheme, the single of realization AMVEP or few signal identification, the identification efficient of raising brain-computer interface, the high efficiency of transmission of guarantee brain-computer interface information.

Description of drawings

Fig. 1 is that hardware of the present invention connects synoptic diagram.

Fig. 2 is a stimulus sequence synoptic diagram of the present invention.

Fig. 3 is that the CRT unifrequency of 100Hz refresh rate stimulates the amplitude-frequency curve chart of SSVEP response down.

Fig. 4 is based on the process flow diagram of which amplitude modulation VEP EEG feature extraction method.

Fig. 5-a is the time domain waveform figure after the data filtering of 1.25Hz modulation.

Fig. 5-b is the FFT spectrogram of data after the filtering.

Fig. 5-c is the amplitude envelope FFT spectrogram of data after the filtering.

Embodiment

Below in conjunction with accompanying drawing the present invention is done further detailed description.

Based on which amplitude modulation VEP brain-computer interface method, may further comprise the steps:

Step 1; With reference to Fig. 1, the Oz position of sound production potential electrode A in the occipital region of subject's head D is at the one-sided ear-lobe position of sound production reference electrode B of subject's head D; At the frons Fpz of subject's head D position of sound production ground electrode C; The output terminal of potential electrode A inserts the first input end E1 of eeg amplifier E, and the output terminal of reference electrode B inserts the second input end E2 of eeg amplifier E, and the output terminal of ground electrode C inserts the 3rd input end E3 of eeg amplifier E; The output terminal of eeg amplifier E links to each other with the input end of computing machine F, and the output terminal of computing machine F and computer screen G are connected to form the eeg signal acquisition system;

Step 2 sees figures.1.and.2, and stimulates normal form to be presented in face of the experimenter through computer screen G the denotable unifrequency of computer screen G; Subject's head D distance calculation screen G is 50～100 centimetres; Wherein, optional frequency receives the screen refresh rate restriction, reaches as high as 100Hz and test the setting of used CRT monitor screen refresh rate at present; Therefore as shown in table 1 in the selectable frequency of 6～50Hz; According to the experimenter different unifrequencys stimulate down bring out potential response amplitude susceptibility rule, the responsive stimuli responsive frequency of optimized choice is as the carrier frequency of modulation VEP stimulation normal form, the optimized choice principle is: according to experimenter's difference; Make the amplitude-frequency response figure of the optional frequency of this experimenter; From amplitude-frequency response figure, select the pairing frequency values of amplitude maximal value as carrier frequency, promptly select the most responsive frequency of experimenter's amplitude-frequency response as carrier frequency, the signal to noise ratio (S/N ratio) of maximization response signal; With reference to Fig. 3, can know that the responsive stimuli responsive frequency of the experimenter who participates in experiment is 12.5Hz;

Table 1: test used CRT monitor and can represent frequency and refresh the frame number corresponding relation

Step 3; According to the selected carrier frequency of step 2; And influence the factor that the modulating wave modulating frequency is selected, and the range of choice of modulating frequency is proposed, comprise screen refresh rate and spectral resolution owing to influence the factor of modulating wave; So carrier frequency must meet sampling thheorem to the modulating wave modulating frequency, the method that the modulating wave modulating frequency is selected specifically comprises following content:

Amplitude modulated signal: y (t)=C+C*sin (2* π * f _m* Δ t _m-pi/2) (1)

Sampling thheorem: $\frac{f_{\mathrm{Ms}}}{f_m}\&GreaterEqual;2.56---\left(2\right)$

The transformation relation of parameter is following in the amplitude modulated signal computing formula:

f _Ms→ f ₀, that is: ${\mathrm{\&Delta;\; t}}_m\&RightArrow;{\mathrm{\&Delta;\; <figure-callout\; id="0"\; label="t"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">t</figure-callout>}}_0=\frac{1}{{\mathrm{<figure-callout\; id="0"\; label="f"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">f</figure-callout>}}_0}=\frac{n}{f_r}---\left(3\right)$

${\mathrm{\&Delta;<figure-callout\; id="0"\; label="t"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">t</figure-callout>}}_0={\mathrm{\&Delta;t}}_m*\mathrm{\&lambda;}\&DoubleRightArrow;\frac{1}{\mathrm{\&lambda;}}=\frac{{\mathrm{\&Delta;t}}_m}{{\mathrm{\&Delta;<figure-callout\; id="0"\; label="t"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">t</figure-callout>}}_0}=\frac{1}{f_{\mathrm{ms}}}/\frac{n}{f_r}=\frac{f_r}{f_{\mathrm{ms}}*n}---\left(4\right)$

Learn that by above formula (1)～(4) derivation final frequency of modulated wave is:

$x=\frac{f_m}{\mathrm{\&lambda;}}=\frac{f_m*f_r}{f_{\mathrm{ms}}*n}\&DoubleRightArrow;\frac{f_{\mathrm{ms}}}{f_m}=\frac{f_r}{n*x}\&GreaterEqual;2.56\&DoubleRightArrow;x\&le;\frac{f_r}{n*2.56}$

Wherein:

C: constant

f _Ms: the SF of amplitude modulated signal

f _m: the frequency of amplitude modulated signal

f ₀: carrier frequency

: the SI of amplitude modulated signal

X: modulation wave frequency

N: the frame number of the required screen-refresh of expression one-period

f _r: screen refresh rate

By above shift onto process learn frequency of modulated wave scope should for:

$0\&le;x\&le;\frac{f_r}{n*2.56}=\frac{{\mathrm{<figure-callout\; id="0"\; label="f"\; filenames="HDA0000088735540000012.png"\; state="\{\{state\}\}">f</figure-callout>}}_0}{2.56};$

As screen-refresh f _r=100Hz;

Carrier frequency $f_{}$ ${}_0=\frac{f_r}{n}=\frac{100}{8}=12.5;$

The scope of frequency of modulated wave should for:

$0\&le;x\&le;\frac{12.5}{2.56}$

Step 4; With reference to Fig. 1, Fig. 2 and Fig. 3, according to the selected carrier frequency of step 2 optimization principles, and the range of choice of frequency of modulated wave that step 3 proposes; Design AMVEP normal form; Produce the stimulus sequence of which amplitude modulation VEP brain-computer interface normal form, be presented in face of the experimenter through computer screen G, the computing formula of stimulus sequence that produces the AMVEP normal form is following:

$y_{\mathrm{AMVEP}}\left(t\right)=C+C*\sin (2*\mathrm{\&pi;}*\frac{n*x}{f_r}-\mathrm{pi}/2);$

Wherein, C is a constant, and x is by being designed frequency of modulated wave, and knows according to step 3:

$x\&le;\frac{f_r}{n*2.56};$

Step 5; With reference to Fig. 4, earlier confirm modulating-coding SSVEP normal form through step 2～3, through step 4 it is presented in face of the experimenter again; The final AMVEP response signal that obtains the experimenter through the described eeg signal acquisition of step 1 system; Modulation signal analysis and feature extracting method that proposition adapts are with it realized single or few the signal identification of AMVEP, specifically may further comprise the steps:

◆ the AMVEP response eeg data to the different stimulated sequence that collects carries out bandpass filtering, with reference to Fig. 5-a;

◆ adopt the Hilbert demodulation method that filtered data are carried out amplitude demodulation;

◆ the data after adopting the FFT method to amplitude demodulation are asked its spectrum maximum, with reference to Fig. 5-b;

◆ according to spectrum results, obtain the maximum value frequency value corresponding in the frequency spectrum, be the frequency of modulated wave value, with reference to Fig. 5-c.

Claims (1)

1. based on which amplitude modulation VEP brain-computer interface method, it is characterized in that, may further comprise the steps:

Step 1; In the occipital region of subject's head (D) Oz position of sound production potential electrode (A); At the one-sided ear-lobe position of sound production reference electrode (B) of subject's head (D), at the frons Fpz of subject's head (D) position of sound production ground electrode (C), the output terminal of potential electrode (A) inserts the first input end (E1) of eeg amplifier (E); The output terminal of reference electrode (B) inserts second input end (E2) of eeg amplifier (E); The output terminal of ground electrode (C) inserts the 3rd input end (E3) of eeg amplifier (E), and the output terminal of eeg amplifier (E) links to each other with the input end of computing machine (F), and the output terminal of computing machine (F) and computer screen (G) are connected to form the eeg signal acquisition system;

Step 2; Stimulate normal form to be presented in face of the experimenter the denotable unifrequency of computer screen (G) through computer screen (G); Subject's head (D) distance calculation screen (G) is 50～100 centimetres; According to the experimenter different unifrequencys stimulate down bring out potential response amplitude susceptibility rule, the responsive stimuli responsive frequency of optimized choice is as the carrier frequency of modulation VEP stimulation normal form, the optimized choice principle is: according to experimenter's difference; Make the amplitude-frequency response figure of the optional frequency of this experimenter; From amplitude-frequency response figure, select the pairing frequency values of amplitude maximal value as carrier frequency, promptly select the most responsive frequency of experimenter's amplitude-frequency response as carrier frequency, the signal to noise ratio (S/N ratio) of maximization response signal;

Step 3; According to the selected carrier frequency of step 2; And influence the factor that the modulating wave modulating frequency is selected, and the range of choice of modulating frequency is proposed, comprise screen refresh rate and spectral resolution owing to influence the factor of modulating wave; So carrier frequency must meet sampling thheorem to the modulating wave modulating frequency, the method that the modulating wave modulating frequency is selected specifically comprises following content:

Amplitude modulated signal: y (t)=C+C*sin (2* π * f _m* Δ t _m-pi/2) (1)

Sampling thheorem: $\frac{f_{\mathrm{Ms}}}{f_m}\&GreaterEqual;2.56---\left(2\right)$

The transformation relation of parameter is following in the amplitude modulated signal computing formula:

f _Ms→ f ₀, that is: ${\mathrm{\&Delta;\; t}}_m\&RightArrow;{\mathrm{\&Delta;\; t}}_0=\frac{1}{f_0}=\frac{n}{f_r}---\left(3\right)$

${\mathrm{\&Delta;t}}_0={\mathrm{\&Delta;t}}_m*\mathrm{\&lambda;}\&DoubleRightArrow;\frac{1}{\mathrm{\&lambda;}}=\frac{{\mathrm{\&Delta;t}}_m}{{\mathrm{\&Delta;t}}_0}=\frac{1}{f_{\mathrm{ms}}}/\frac{n}{f_r}=\frac{f_r}{f_{\mathrm{ms}}*n}---\left(4\right)$

Learn that by above formula (1)～(4) derivation final frequency of modulated wave is:

$x=\frac{f_m}{\mathrm{\&lambda;}}=\frac{f_m*f_r}{f_{\mathrm{ms}}*n}\&DoubleRightArrow;\frac{f_{\mathrm{ms}}}{f_m}=\frac{f_r}{n*x}\&GreaterEqual;2.56\&DoubleRightArrow;x\&le;\frac{f_r}{n*2.56}$

Wherein:

C: constant

f _Ms: the SF of amplitude modulated signal

f _m: the frequency of amplitude modulated signal

f ₀: carrier frequency

: the SI of amplitude modulated signal

X: modulation wave frequency

N: the frame number of the required screen-refresh of expression one-period

f _r: screen refresh rate

By above shift onto process learn frequency of modulated wave scope should for:

$0\&le;x\&le;\frac{f_r}{n*2.56}=\frac{f_0}{2.56};$

Step 4; According to the selected carrier frequency of step 2 optimization principles; And the range of choice of frequency of modulated wave that step 3 proposes, design AMVEP normal form, the stimulus sequence of generation which amplitude modulation VEP brain-computer interface normal form; Be presented in face of the experimenter through computer screen (G), the computing formula of stimulus sequence that produces the AMVEP normal form is following:

$y_{\mathrm{AMVEP}}\left(t\right)=C+C*\sin (2*\mathrm{\&pi;}*\frac{n*x}{f_r}-\mathrm{pi}/2);$

Wherein, C is a constant, and x is by being designed frequency of modulated wave, and knows according to step 3:

$x\&le;\frac{f_r}{n*2.56};$

Step 5; Confirm modulating-coding SSVEP normal form through step 2～3 earlier; Through step 4 it is presented in face of the experimenter again, finally obtains experimenter's AMVEP response signal, propose the modulation signal analysis and the feature extracting method that adapt with it through the described eeg signal acquisition of step 1 system; Realize single or few the signal identification of AMVEP, specifically may further comprise the steps:

◆ the AMVEP response eeg data to the different stimulated sequence that collects carries out bandpass filtering;

◆ adopt the Hilbert demodulation method that filtered data are carried out amplitude demodulation;

◆ the data after adopting the FFT method to amplitude demodulation are asked its spectrum maximum;

◆ according to spectrum results, obtain the maximum value frequency value corresponding in the frequency spectrum, be the frequency of modulated wave value.

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