CN113180706B - FHN stochastic resonance-based SSVEP characteristic frequency extraction method - Google Patents
FHN stochastic resonance-based SSVEP characteristic frequency extraction method Download PDFInfo
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Abstract
The SSVEP characteristic frequency extraction method based on FHN stochastic resonance comprises the steps of firstly carrying out multichannel data acquisition, then carrying out signal preprocessing, namely adopting a common average reference algorithm to reduce the dimension of multichannel signals, and filtering low-frequency noise by using a Butterworth filter; then, FHN stochastic resonance parameter initialization and model processing are carried out, the preprocessed signals and noise are sent into the FHN stochastic resonance model to carry out stochastic resonance processing, and then, a spectrogram of the SSVEP with enhanced noise is calculated through fast Fourier transform so as to identify target frequency; then, peak frequency identification is carried out, and finally, frequency matching detection is carried out; the invention realizes high-precision identification of the characteristic frequency.
Description
Technical Field
The invention relates to the technical field of brain-computer interfaces, in particular to an SSVEP characteristic frequency extraction method based on FHN stochastic resonance.
Background
The brain-computer interface technology realizes the direct control of the human brain to the external equipment by decoding the movement intention contained in the brain electrical signal and converting the movement intention into different driving commands. As a novel man-machine interaction means, the brain-machine interface brings new hopes of autonomous life for patients with partial nerve necrosis, cerebral apoplexy, high amputation, severe paralysis and the like. The steady-state visual evoked potential is a group of specific brain electrical signals generated in a brain occipital lobe area of human eyes after receiving visual stimulus, and has the characteristics of stable period, obvious characteristics and no need of training compared with P300, motor imagery signals and spontaneous brain electrical signals, and becomes one of the most common control signals of a brain-computer interface.
At present, most SSVEP extraction methods are established under a linear framework, noise is regarded as harmful information, the signal-to-noise ratio is highlighted by suppressing the noise, and the detection capability of weak signals is improved. Although, these methods can extract information contained in the original EEG to different degrees under the condition of low signal-to-noise ratio, and show a certain SSVEP detection capability, the following problems cannot be avoided: (1) In order to eliminate the multi-scale noise contained in the EEG, a suitable bandpass filter needs to be selected, the edge effect of the bandpass filter reduces the effective data length and increases the detection time, and furthermore, the adaptive matching of the passband range of the bandpass filter and the signal characteristic frequency needs to be considered. (2) The use of linear methods to extract SSVEPs with significant non-linear and non-stationary characteristics also attenuates or loses useful signals while noise is suppressed. When the stability of the induced signal is insufficient, the useful signal is suppressed to a degree far exceeding the suppression noise even. Thus, the information contained in the original EEG cannot be fully utilized, affecting the detection sensitivity and recognition accuracy.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an SSVEP characteristic frequency extraction method based on FHN stochastic resonance, which utilizes noise contained in multichannel EEG to enhance a spectrum diagram of SSVEP, combines the characteristics that FHN output frequency response is equivalent to a group of nonlinear band-pass filters and passband range is adjustable, and sends an original EEG signal into an FHN aperiodic stochastic resonance model to carry out noise enhancement so as to retain all information of SSVEP, thereby realizing high-precision identification of characteristic frequency.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
an SSVEP characteristic frequency extraction method based on FHN stochastic resonance comprises the following steps:
1) Multichannel data acquisition: collecting multi-channel EEG signals of a tested person; the multichannel EEG signals are subjected to amplification, filtering and digital-analog conversion;
2) Signal pretreatment:
2.1 Multi-channel signal dimension reduction: adopting a common average reference algorithm to reduce the dimensionality of the multi-channel signal;
2.2 Low pass filtering: filtering low-frequency noise by using a Butterworth filter;
3) FHN stochastic resonance parameter initialization and model processing: setting calculation parameters including model parameters epsilon and maximum peak order N to be identified;
sending the preprocessed SSVEP signal with noise interference to a corresponding model to perform FHN stochastic resonance processing, and calculating a spectrogram of the noise enhanced SSVEP through fast Fourier transform to identify a target frequency;
4) Peak frequency identification: extracting characteristic frequencies corresponding to the N-th order main peak from the spectrogram of the output signal obtained in the step 3);
5) Frequency matching detection: matching the identification frequency with all the stimulation frequencies, and if the matching is successful, effectively identifying the target frequency; if the matching fails, detecting whether the currently identified order is greater than the set maximum order; if the termination condition is satisfied, the detection is ended, indicating that the target frequency identification fails; otherwise, the calculation returns to step 4).
The collecting electrodes in the multi-channel EEG signal collection in the step 1) are arranged according to a 10/20 electrode distribution standard, a reference electrode (Ref) is positioned on the forehead (FPz) of the brain, a ground electrode (GND) is positioned on a single-side left earlobe (A1), eight channels of OZ, O1, O2, POZ, PO3, PO4, PO5 and PO6 are used for recording the EEG signals, and the sampling frequency of each lead is 250Hz.
In the step 2.1), the OZ channel is used as a reference channel, and the average value of four channels of PO5, PO3, PO6 and O2 is selected as a common average reference channel.
The pass band ripple is set to 1 and the stop band ripple is set to 10 in the step 2.2).
The mathematical expression of the FHN stochastic resonance model in the step 3) is as follows:
wherein: v (t) -cell membrane voltage, a fast variable; w (t), the concentration of ions in the membrane, is a slow variable; a-is a constant to represent excitation amplitude, which promotes the periodic ignition of neurons; epsilon-time parameter constant, which determines the firing rate of the neuron, here a value of 0.04; b-parameter constant, value 0.15; n (t) -gaussian white noise, with zero mean and autocorrelation function satisfying < n (t) n(s) > = 2D delta (t-s); <. -overall mean; s (t) -an input non-periodic excitation signal, and a fourth-order Runge-Kuta method is adopted when the differential equation set is solved;
let v (t) =v (t) ' +1/2,w (t) =w (t) ' -b+1/2, a=a ' -b+1/2, the fhn stochastic resonance model is reduced to the following form:
wherein:-a threshold voltage; b-distance of signal amplitude to threshold voltage;
let A T -b=0, thenOnly the model parameters epsilon and the maximum peak order N to be identified need to be set and adjusted.
The beneficial effects of the invention are as follows:
(1) The FHN stochastic resonance processing of the invention considers the influence of random noise on the system output, further strengthens the suppression of noise, and obtains smoother signals.
(2) The FHN stochastic resonance processing output frequency response of the invention is similar to a group of nonlinear band-pass filters, only has one adjustable model parameter epsilon and adjustable passband range, and is suitable for suppressing SSVEP multi-scale noise.
(3) The invention utilizes noise energy to enhance the SSVEP, avoids damaging useful signals and the edge effect of a filter, and improves the identification accuracy of the characteristic frequency of the SSVEP.
Drawings
Fig. 1 is a flow chart of the present invention.
Figure 2 is a plot of FHN stochastic resonance output signals for different parameters obtained when the model parameters epsilon of the present invention affect stochastic resonance output.
Fig. 3 is a CCA coefficient spectrum of an EEG signal and a template signal obtained by filtering low frequency components below 8Hz when the characteristic frequency is identified by a conventional CCA method.
FIG. 4 is a graph of EEG signal spectrum obtained by filtering low frequency components below 8Hz when the characteristic frequency is identified by the FHN stochastic resonance method of the present invention.
Figure 5 is a graph showing the results of the characteristic frequency identification accuracy obtained by the conventional CCA method and FHN stochastic resonance method of the present invention for 15 normal subjects.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Referring to fig. 1, an SSVEP characteristic frequency extraction method based on FHN stochastic resonance includes the following steps:
1) Multichannel data acquisition: the subject was subjected to multichannel EEG signal acquisition by a g.usbamp (g.tec inc., austria) electroencephalogram acquisition system, with the single-sided earlobe grounded during EEG acquisition and at the frontal location (FPz) as a reference electrode. Eight channels of OZ, O1, O2, POZ, PO3, PO4, PO5 and PO6 close to occipital leaves record brain electrical signals, and the sampling frequency of each lead is 250Hz; the electrode is connected with the input of the electroencephalogram acquisition system, and the electroencephalogram acquisition system outputs electroencephalogram data through amplification, filtering and digital-analog conversion processing and is connected with the input of the data processing module;
analysis of the influence of model parameter ε on stochastic resonance output: referring to fig. 2, a group of standard sinusoidal simulation signals (sinusoidal signal amplitude a=5, frequency f=0.5 HZ, sampling frequency fs=1000 HZ, sampling point number n=10000) are established, noise with a certain d=20 is added as input of a model, FHN stochastic resonance processing is performed, system parameters are adjusted to obtain system outputs with different performances, and thus, output signals of the FHN stochastic resonance system under the input of the obtained different model parameters can be seen from the figure, when epsilon=0.01, the output signals have larger stochastic fluctuation, random interference of the noise plays a leading role at the moment, and burrs of the obtained signals are large; as the model parameter epsilon increases to 0.04, the fluctuation component in the output signal is gradually inhibited, and the response of the system is improved; however, the overlarge model parameter epsilon can cause the output state of the system not to keep up with the response speed of the input signal in the transfer process, and the waveform of the output signal is distorted; simultaneously, the amplitude of noise and driving signals are also greatly filtered, so that output signals are distorted; therefore, for different input signals, an optimal model parameter epsilon exists, so that the FHN stochastic resonance system has the best filtering effect;
2) Signal pretreatment:
2.1 Multi-channel signal dimension reduction: in order to fully utilize the information contained in each channel, the data processing module adopts a common average reference algorithm to reduce the dimension of the multi-channel signal, takes an OZ channel as a reference channel, and selects the average value of four channels PO5, PO3, PO6 and O2 as the common average reference channel;
2.2 Low pass filtering: filtering low-frequency noise by using a Butterworth filter, setting the passband ripple to be 1, setting the stopband ripple to be 10, and preventing the interference of the low-frequency noise on the identification characteristic frequency;
3) FHN stochastic resonance parameter initialization and model processing: setting calculation parameters according to the characteristics of the acquired signals and the actual analysis requirements, wherein the calculation parameters comprise model parameters epsilon and the maximum peak value order N to be identified;
the mathematical expression of the FHN stochastic resonance model is:
wherein: v (t) -cell membrane voltage, a fast variable; w (t), the concentration of ions in the membrane, is a slow variable; a-is a constant to represent excitation amplitude, which promotes the periodic ignition of neurons; epsilon-time parameter constant, which determines the firing rate of neurons, here the value is 0.04, the same applies; b-parameter constant, value 0.15; n (t) -gaussian white noise, with zero mean and autocorrelation function satisfying < n (t) n(s) > = 2D delta (t-s); <. > -solving for the global average; s (t) -an input non-periodic excitation signal, and a fourth-order Runge-Kuta method is adopted when the differential equation set is solved;
let v (t) =v (t) ' +1/2,w (t) =w (t) ' -b+1/2, a=a ' -b+1/2, the fhn stochastic resonance model is reduced to the following form:
wherein:-a threshold voltage; b-distance of signal amplitude to threshold voltage;
let AT-b=0, thenOnly the model parameter epsilon and the maximum peak order N to be identified need to be set and adjusted;
sending the preprocessed SSVEP signal with noise interference into an FHN stochastic resonance model for FHN stochastic resonance processing, and calculating a spectrogram of the noise enhanced SSVEP through fast Fourier transform to identify a target frequency;
4) Peak frequency identification: extracting characteristic frequencies corresponding to the N-th order main peak from the spectrogram of the output signal obtained in the step 3);
5) Frequency matching detection: matching the identification frequency with all the stimulation frequencies, and if the matching is successful, effectively identifying the target frequency; if the match fails, it is necessary to detect whether the currently identified order is greater than the set maximum order; if the termination condition is satisfied, the detection is ended, indicating that the target frequency identification fails; otherwise, the calculation returns to step 4).
The invention will now be described with reference to examples.
15 normal subjects (10 men and 5 women, all 20-26 years old) were tested by the method of the invention. The light flicker is used as a visual stimulation paradigm, the visual stimulation paradigm comprises 40 targets, the stimulation frequencies are respectively 8-16Hz, and the interval is 0.2Hz; simultaneously rendering 40 visual flashes (3 cm long by 3cm wide) on a 24 inch LCD display of a Dell-S2409W computer at a refresh rate of 75 Hz; the horizontal interval and the vertical interval between the two stimulations are respectively 2cm and 3cm; the head of the user is 150cm from the computer screen. The characteristic frequency spectrograms of the SSVEP signals under the conventional CCA method and the FHN stochastic resonance method are respectively output, the recognition accuracy is calculated, the obtained extraction effects are respectively shown in a figure 3 and a figure 4 (8.4 Hz, 8.6Hz, 12.4Hz, 14.6Hz, 14.8Hz and 15Hz in the figure 3 represent the characteristic frequency recognition errors, 9.4Hz, 11.2Hz, 12.8Hz and 15.4Hz in the figure 3 represent a large number of interference peaks around the characteristic frequency, 8.4Hz, 8.6Hz, 12.4Hz, 14.8Hz and 15Hz in the figure 4 represent the characteristic frequency extraction results to be corrected, and 9.4Hz, 11.2Hz, 12.8Hz and 15.4Hz in the figure 4 represent the interference peaks to be well inhibited). The characteristic frequency identification accuracy of 15 testees is shown in fig. 5, and compared with a CCA method, the characteristic frequency identification effect of the FHN stochastic resonance on EEG signals is greatly improved. For signals with larger interference peaks around the target frequency, such as spectrograms corresponding to 9.4Hz, 11.2Hz, 12.8Hz and 15.4Hz, the interference frequency is more obviously inhibited after FHN resonance treatment, and the dominant position of the target frequency in the spectrograms is further highlighted; especially, for the signals which cannot be identified by CCA, such as the spectrograms corresponding to 8.4Hz, 8.6Hz, 12.4Hz, 14.8Hz and 15Hz, the FHN stochastic resonance can effectively identify the corresponding target frequency. Meanwhile, the average processing time for identifying the SSVEP characteristic frequency by utilizing the CCA is 2.79s, and the average processing time for identifying the SSVEP characteristic frequency by utilizing the FHN stochastic resonance is 1.24s, so that the processing speed of the extraction method is greatly reduced.
The invention can start from the visual central nervous system, apply brain-computer interface technology, realize higher recognition precision and faster processing speed in the SSVEP multi-scale noise suppression and characteristic frequency extraction method, effectively increase the information transmission rate of the BCI system based on the SSVEP, and provide an effective means for the characteristic frequency extraction of the SSVEP.
Claims (5)
1. The SSVEP characteristic frequency extraction method based on FHN stochastic resonance is characterized by comprising the following steps of:
1) Multichannel data acquisition: collecting multi-channel EEG signals of a tested person; the multichannel EEG signals are subjected to amplification, filtering and digital-analog conversion;
2) Signal pretreatment:
2.1 Multi-channel signal dimension reduction: adopting a common average reference algorithm to reduce the dimensionality of the multi-channel signal;
2.2 Low pass filtering: filtering low-frequency noise by using a Butterworth filter;
3) FHN stochastic resonance parameter initialization and model processing: setting calculation parameters including model parameters epsilon and maximum peak order N to be identified;
sending the preprocessed SSVEP signal with noise interference to a corresponding model for FHN stochastic resonance processing, and calculating a spectrogram of the noise enhanced SSVEP through fast Fourier transform to identify a target frequency;
4) Peak frequency identification: extracting characteristic frequencies corresponding to the N-th order main peak from the spectrogram of the output signal obtained in the step 3);
5) And (3) rate matching detection: matching the identification frequency with all the stimulation frequencies, and if the matching is successful, effectively identifying the target frequency; if the matching fails, detecting whether the currently identified order is greater than the set maximum order; if the termination condition is satisfied, the detection is ended, indicating that the target frequency identification fails; otherwise, the calculation returns to step 4).
2. The method for extracting the characteristic frequency of the SSVEP based on FHN stochastic resonance according to claim 1, wherein the method comprises the following steps: the collecting electrodes in the multi-channel EEG signal collection in the step 1) are arranged according to a 10/20 electrode distribution standard, a reference electrode (Ref) is positioned on the forehead (FPz) of the brain, a ground electrode (GND) is positioned on a single-side left earlobe (A1), eight channels of OZ, O1, O2, POZ, PO3, PO4, PO5 and PO6 are used for recording the EEG signals, and the sampling frequency of each lead is 250Hz.
3. The method for extracting the characteristic frequency of the SSVEP based on FHN stochastic resonance according to claim 2, wherein the method comprises the following steps: in the step 2.1), the OZ channel is used as a reference channel, and the average value of four channels of PO5, PO3, PO6 and O2 is selected as a common average reference channel.
4. The method for extracting the characteristic frequency of the SSVEP based on FHN stochastic resonance according to claim 1, wherein the method comprises the following steps: the pass band ripple is set to 1 and the stop band ripple is set to 10 in the step 2.2).
5. The method for extracting the characteristic frequency of the SSVEP based on FHN stochastic resonance according to claim 1, wherein the method comprises the following steps: the mathematical expression of the FHN stochastic resonance model in the step 3) is as follows:
wherein: v (t) -cell membrane voltage, a fast variable; w (t), the concentration of ions in the membrane, is a slow variable; epsilon-time parameter constant, which determines the firing rate of the neuron, here a value of 0.04; b-parameter constant, value 0.15; n (t) -gaussian white noise, with zero mean and autocorrelation function satisfying < n (t) n(s) > = 2D delta (t-s); <. > -solving for the global average; s (t) -an input non-periodic excitation signal, and a fourth-order Runge-Kuta method is adopted when the differential equation set is solved;
let v (t) =v (t) ' +1/2,w (t) =w (t) ' -b+1/2, a=a ' -b+1/2, the fhn stochastic resonance model is reduced to the following form:
wherein:-a threshold voltage; b-distance of signal amplitude to threshold voltage;
let A T -b=0, thenOnly the model parameters epsilon and the maximum peak order N to be identified need to be set and adjusted.
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