CN115826743B - Multi-channel brain-computer signal modeling method for SSVEP brain-computer interface - Google Patents
Multi-channel brain-computer signal modeling method for SSVEP brain-computer interface Download PDFInfo
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Abstract
The application discloses a multi-channel brain-computer signal modeling method facing an SSVEP brain-computer interface, which comprises the following steps: determining the SSVEP signal frequency range and the signal number of a tested person; based on the signal number, a basic neuron group model is established for generating a plurality of rhythmic narrow-band signals in a frequency range; determining a multiple dynamic neuron group model based on a frequency range with multiple rhythmic narrowband signals, for modulating a single-channel SSVEP frequency signal suitable for a tested person; a multi-channel multi-dynamic neuron group model is built based on the multi-dynamic neuron group model and the single-channel SSVEP frequency signals and is used for setting a coupling coefficient matrix, so that multi-channel electroencephalogram signals are built by adjusting coupling coefficients among different basic neuron groups, and the difference among tested persons is reflected. The application generates the multi-channel brain-computer signal facing the SSVEP brain-computer interface applied to the brain-control intelligent instrument from the angle of the model, and solves the problem of high cost in the prior art.
Description
Technical Field
The application relates to the field of signal processing, in particular to a multi-channel brain-computer signal modeling method for an SSVEP brain-computer interface.
Background
The brain-computer interface (Brain Computer Interface, BCI) provides a direct real-time information communication and control channel for the user and the external physical equipment, and can directly decode the brain activities of the user from the neurophysiologic signals to control instructions for the external equipment. The BCI technology completes the acquisition of brain electrical signals, the preprocessing of data, the feature extraction, the classification and the command quantification. For the acquisition of the brain electrical signals, the data acquired by the brain electrical signals are only the activities of partial neurons, but the specific activity conditions in the brain are infinite, so that in order to solve the problem, researchers mainly acquire the brain electrical signals through two invasive and non-invasive means.
Invasive methods are somewhat invasive, and compared to invasive methods, non-invasive methods are less expensive, and non-invasive methods are widely used in current research and are readily accepted by the general public. Whereas, among the non-invasive methods, the most widely used is electroencephalogram (EEG). EEG is a comprehensive representation of the postsynaptic currents of a large number of neuronal populations in brain tissue and can be broken down into specific frequency ranges (delta: 1-4Hz, theta: 4-8Hz, alpha: 8-12Hz, beta: 12-30Hz, gamma: 30-70 Hz). The monitoring method is that electrodes are placed along the scalp, and then through a plurality of electrodes placed on the scalp, spontaneous electrical activity of the brain is recorded in a period of time. Although electroencephalogram has limited spatial resolution and more signal artifacts, it is still a valuable tool for research and diagnosis.
Steady state visual stimulus (SSVEP) refers to EEG brain electrical signals that the brain would induce for a particular frequency of visual stimulus, and when the retina receives 3.5Hz to 75Hz visual stimulus, the brain would produce electrical activity at the same frequency or multiple of the frequency of visual stimulus. BCI technology based on SSVEP-EEG has been widely used to develop various brain-controlled intelligent appliances such as brain-controlled cursors, brain-controlled virtual keyboards, brain-controlled browsing web pages, brain-controlled prostheses, brain-controlled wheelchairs, brain-controlled vehicles, brain-controlled robots, and the like. In the research of the brain control intelligent instrument, the subjects still need to be recruited, and operations such as wearing an acquisition instrument, even smearing conductive paste, cleaning hair and the like are needed, so that a certain amount of manpower and material resources are consumed, and how to generate correct, similar and applicable brain electrical signals by simulation modeling with lower cost is very important and significant.
Various neural models have been proposed and generally fall into two categories. One class is detailed models, i.e., modeling neurons. It is clear that the information transfer is done by many neurons cooperating with each other, and that the study of the firing of individual neurons is far from efficient for studying the complex behaviour of the brain, only at a microscopic level. Thus, researchers have studied the neural network composed of a plurality of neurons directly to study the discharging behavior of the neural network, but the variety of neurons is numerous, it is difficult to determine the parameters of each neuron model, and the connection between various neurons is very complex and the amount of computation is huge, so it is quite difficult to simulate the actual neural network at the neuron level. Another class of neural models is the neural group model, which does not require modeling of individual cells in a network structure, but rather models the overall characteristics of a group of neurons consisting of a specific class of cells.
The main idea of the nerve group model is 'average area approximation', namely, the model adopts lumped state variables to represent the average behavior of the whole cell group in the nerve network, and the model is simple and has physiological significance, and is a model for constructing brain electrical signals from the perspective of a 'tissue structure' of a nerve system. The coupled neural population model may reflect the interrelation between the neural population, and may simulate a neural network of large scale interactions at a macroscopic level.
Researchers in Jasen et al first proposed brain electrical signal and visual evoked potential generation in a brain cortical column coupled mathematical model in 1995. On this basis, it is possible to link the BCI to physiological models, in particular brain-computer interfaces based on the SSVEP paradigm. The key technology of the electric signal model is how to enable the model to accurately simulate the multi-channel brain-computer electric signal facing the SSVEP brain-computer interface. The model needs to be considered from several angles of signal frequency, differentiation of different subjects.
Disclosure of Invention
According to the application, a basic neuron group model is constructed, and excitatory or inhibitory subgroups are weighted and controlled in parallel by a plurality of linear conversion functions with different dynamics characteristics, so that signals with richer frequencies and wider frequency bands are generated, the method is applicable to signal frequencies required by an SSVEP range, and different tested objects are distinguished by adjusting the coupling coefficients of the multi-channel and multi-dynamic neuron group model.
In order to achieve the above purpose, the application discloses a multi-channel electroencephalogram signal modeling method facing an SSVEP brain-computer interface, which comprises the following steps:
determining the SSVEP signal frequency range and the signal number of a tested person;
based on the frequency range and the signal number, establishing a basic neuron group model for generating a plurality of rhythmic narrow-band signals of the frequency range;
determining a multiple dynamic neuron population model for modulating a single channel SSVEP frequency signal suitable for a subject based on the frequency range having a plurality of the rhythmic narrowband signals;
and constructing a multi-channel multi-dynamic neuron group model based on the multi-dynamic neuron group model and the single-channel SSVEP frequency signals, wherein the multi-channel multi-dynamic neuron group model is used for setting a coupling coefficient matrix, and further, the difference between the tested persons is reflected by adjusting the coupling coefficients among different basic neuron groups.
Preferably, the multi-dynamic neuron population model can define the required electroencephalogram signal frequency through setting of weight coefficients.
Preferably, the method for obtaining the SSVEP signal frequency range and the signal number includes: obtaining an electroencephalogram signal of a tested person through SSVEP stimulation induction; selecting a subject-required SSVEP frequency from the electroencephalogram signal; modulating the corresponding frequency of the SSVEP stimulation, and determining the frequency range and the signal number of the SSVEP signal suitable for controlling the brain-controlled intelligent instrument.
Preferably, the method of generating the rhythmic narrowband signal comprises: parameters of excitatory and inhibitory linear transformation functions are adjusted using the basic neuron population model and the narrowband signals of different frequencies are generated.
Preferably, the method for modulating the single-channel SSVEP frequency signal comprises the following steps: according to the selected frequencies of the subject in the frequency range, the required combination of excitation and suppression parameters of the multiple dynamic neuron population model and the linear conversion function thereof is determined, and the single-channel SSVEP frequency signal is modulated by adjusting weights.
Preferably, the method for modulating the single-channel SSVEP frequency signal comprises the following steps: the relative proportion of the signals of the cortex area with different dynamics characteristics is adjusted by adjusting the weights and setting the excitation and inhibition parameters of a plurality of different linear conversion functions through the multi-dynamic neuron group model, so that the single-channel SSVEP frequency signals with richer frequencies and wider frequency bands are generated.
Preferably, the method for distinguishing the variability comprises: the multi-channel multi-dynamic neuron group model realizes the coupling of different intensities between brain-electrical signals with different frequencies by setting a coupling coefficient matrix, selects a channel facing an SSVEP brain-computer interface, adjusts the coupling coefficient between the channels through the multi-channel multi-dynamic neuron group model after the channel selection is completed, further modulates the brain-electrical signals of different subjects, and the difference is distinguished by adjusting the coupling coefficient.
Preferably, the parameters of the multi-channel multi-dynamic neuron population model include: average excitatory synaptic gain, average inhibitory synaptic gain, average time constant of excitatory membrane and average time delay of dendrite, average time constant of inhibitory membrane, average time delay of dendrite, average number of synaptic connections on excitatory feedback loop, average number of synaptic connections on inhibitory feedback loop, non-linear S-function parameter, several pairs of weight coefficients and coupling coefficient to other channels.
Compared with the prior art, the application has the following beneficial effects:
the application can generate narrowband signals of various rhythms by adjusting excitability and inhibitory parameters in a transfer function, adjusts the relative proportion of signals of cortex areas with different dynamics characteristics, thereby generating signals with richer frequency and wider frequency band, adjusting the proper SSVEP frequency, adjusting the coupling coefficient among channels by the model, and distinguishing different subjects.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method in a first embodiment of the application;
FIG. 2 is a schematic diagram showing the effect of the internal neural concussion basic neuron population model in the first embodiment of the present application;
FIG. 3 is a schematic diagram illustrating the effect of a multi-channel and multi-dynamic neuron population model for generating neural concussion in a channel according to an embodiment of the present application;
fig. 4 is a schematic diagram of a neural group model of multi-channel coupling according to a second embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1, a flowchart of a method according to a first embodiment of the present application includes: determining the SSVEP signal frequency range and the signal number of a tested person; based on the frequency range and the signal number, a basic neuron group model is established for generating a plurality of rhythmic narrow-band signals of the frequency range; determining a multiple dynamic neuron group model based on a frequency range with multiple rhythmic narrowband signals, for modulating a single-channel SSVEP frequency signal suitable for a tested person; a multi-channel multi-dynamic neuron group model is built based on the multi-dynamic neuron group model and the single-channel SSVEP frequency signals and is used for setting a coupling coefficient matrix, so that multi-channel electroencephalogram signals are built by adjusting coupling coefficients among different basic neuron groups, and the difference among tested persons is reflected.
The first embodiment is directed to practical application of the brain control intelligent instrument, and develops a method for establishing an SSVEP brain electrical signal model, wherein the method can be expanded from establishment of a basic neuron group model to a multi-dynamic neuron group model, and finally, establishment of a multi-channel multi-dynamic neuron group model is realized; the basic neuron population model is used for adjusting parameters of excitatory and inhibitory linear transformation functions and generating narrow-band signals with different frequencies; the multi-dynamic neuron group model can adjust the relative proportion of signals of the cortex area with different dynamics characteristics by adjusting the weight and setting the excitation and inhibition parameters of a plurality of different linear conversion functions, so that signals with richer frequencies and wider frequency bands are generated; the multichannel multi-dynamic neuron group model is used for realizing the coupling of different intensities between the brain electrical signals with different frequencies by setting a coupling coefficient matrix. The coupling between different areas of the brain can be achieved without modeling individual cells in the network structure, but modeling the overall characteristics of the neuronal population consisting of specific types of cells.
In one embodiment, the parameter information of the multi-channel electroencephalogram model includes average excitatory synaptic gain, average inhibitory synaptic gain, excitatory membrane average time constant and dendritic average time delay, inhibitory membrane average time constant and dendritic average time delay, average number of synaptic connections on the excitatory feedback loop, average number of synaptic connections on the inhibitory feedback loop, non-linear S-function parameter, pairs of weighting coefficients, and coupling coefficients to other channels.
The method operation steps of the present application will be described in detail with reference to the present embodiment.
First, the subject controls brain-controlled intelligent devices by "staring at" SSVEP stimulation to induce brain electrical signals, such as: staring at a square with a certain frequency and flickering, the frequency and the harmonic thereof can be identified in the electroencephalogram signals collected by the visual area, so that the control end knows that the subject finishes one-time selection, and the brain-computer interface selects the SSVEP frequency required by the subject; modulating the frequency corresponding to the corresponding stimulation, and determining the SSVEP signal frequency range and the signal number suitable for controlling the brain-controlled intelligent instrument;
and then, establishing a basic neuron group model according to the determined physiological significance of the signal number and the parameter values of the typical waveforms of the generated delta, alpha and gamma waves, as shown in fig. 2. By adjusting the excitatory and inhibitory parameters in the transfer function, a plurality of rhythmic narrowband signals corresponding to the frequency ranges in the steps described above are generated. Determining a desired multiple dynamic neuron population model based on the selected frequencies of the subject over the frequency range, as shown in fig. 3; and the combination of excitation and suppression parameters of the linear conversion function thereof, and by adjusting the weights, a suitable single-channel SSVEP frequency signal is modulated.
Finally, SSVEP brain-computer interface channels facing the brain-control intelligent instrument are selected, after the channels are selected, the coupling coefficients among the channels are adjusted through the model, so that different subjects are distinguished, and the tested diversity can be reflected by adjusting the coupling coefficients.
In the first embodiment, the signal frequency adjustment process of the multiple dynamic neuron population model to the basic neuron population model is as follows: the relative proportion of signals of the cortex area with different dynamics characteristics is adjusted by adjusting the weight W through the multiple dynamic neuron group model and setting the excitation and inhibition parameters of a plurality of different linear conversion functions, so that signals with richer frequencies and wider frequency bands are generated. In addition, the multi-dynamic neuron population model can limit the required electroencephalogram signal frequency through setting of weight coefficients.
It should be noted that, in the first embodiment, the generating process of the multi-channel electroencephalogram model includes: the multi-channel multi-dynamic neuron group model is used for realizing the coupling of different intensities between brain-electrical signals with different frequencies by setting a coupling coefficient matrix, selecting channels facing an SSVEP brain-computer interface, adjusting the coupling coefficients between the channels through the model after the channel selection is completed, further modulating the brain-electrical signals of different subjects, and distinguishing the tested differences by adjusting the coupling coefficients.
Example two
In the second embodiment, focusing on the modeling method of the above-mentioned multi-channel multi-dynamic neuron population model, the steps include:
s1, an excitatory or inhibitory dynamic linear transformation function: the average pulse density of action potential is converted into postsynaptic average membrane potential, and the unit step response of the dynamic linear transformation function is as follows:
wherein: t is a time constant, u (t) is a unit step function, H e 、H i Average excited synaptic gain and average suppressed synaptic gain, a e 、a i H is the reciprocal of the excitatory membrane average time constant and the dendrite average time delay, the reciprocal of the inhibitory membrane average time constant and the dendrite average time delay, respectively e (t)、h i (t) is the mean postsynaptic membrane potential of excitation and the mean postsynaptic membrane potential of inhibition, respectively, and e is the base of a natural logarithmic function.
S2, static nonlinear function: the presynaptic average membrane potential is converted into an average pulse density of action potentials, as shown in the formula:
wherein: s (v) is: static nonlinear function, 2e 0 For maximum ignition rate, v 0 For relative to ignition rate e 0 R represents the degree of curvature of the sigmoid function. v is the presynaptic average membrane potential. The static nonlinear functions in the neuron population model are all uniform.
S3All external inputs from the adventitious and subcortical regions are represented by Gauss distributed excitation inputs p (t). The average number of synaptic connections between a population of pyramidal cells and a population of interneurons is determined by the connection constant C 1 ,C 2 ,C 3 ,C 4 And (3) representing. In summary, the basic neural cell population model can be represented by the following differential equation:
wherein: y is 0 ,y 1 ,y 2 ,y 3 ,y 4 ,y 5 Output signal of subgroup 1, excitatory feedback of subgroup 2, inhibitory feedback of subgroup 2, y, respectively 0 First derivative of (y) 1 First derivative of (y) 2 First derivative of H e 、H i Average excitatory synaptic gain and average inhibitory synaptic gain, ae and ai are the average time constant of excitatory membrane and the inverse of dendritic average time delay, the average time constant of inhibitory membrane and the inverse of dendritic average time delay, respectively, C 1 ,C 2 ,C 3 ,C 4 For the connection constant, mean synaptic connection number between pyramidal cell population and interneuron population is expressed, S (y 1 (t)-y 2 (t)) is the average density that converts the presynaptic average membrane voltage of subgroup 1 into an action potential.
S4, brain functions are formed by strong coupling between remote areas, and the cerebral cortex is excitatory for output of remote targets. Wherein q jk The coupling coefficient of the j channel to the k channel is expressed, and in summary, the multi-channel multi-dynamic nerve group model can be expressed as follows by differential equation:
wherein: y is 0 ,y 1 ,y 2 ,y 3 ,y 4 ,y 5 The ith output signal of j-channel subgroup 1, the ith excitatory feedback of j-channel subgroup 2, respectivelyIth inhibitory feedback, y 0 First derivative of (y) 1 First derivative of (y) 2 First derivative of H e 、H i Average excited synaptic gain and average suppressed synaptic gain, a e 、a i C is the reciprocal of the excitatory membrane average time constant and the dendrite average time delay, the reciprocal of the inhibitory membrane average time constant and the dendrite average time delay, respectively 1 ,C 2 ,C 3 ,C 4 The connection constant represents the average number of synaptic connections between the pyramidal cell population and the interneuron population. S (-) represents a static nonlinear function, in particular, S (C) 3 ∑W ji y 0 ji ) Representing the conversion of the inhibitory film voltage combined by the 1 st to N inhibitory feedback of the j-channel subgroup 2 to an average density, S kj The coupling signals input to the j channel for the other channels.
S5, a brain control operator controls the brain control intelligent instrument according to own control intention, stares at a corresponding stimulation interface (in an SSVEP mode) to generate a corresponding brain-computer signal, and the brain-computer interface analyzes the brain-computer signal to obtain the control intention of the operator and outputs a corresponding identification control command. Record the required frequency of the tested person and pass S 1 、S 2 、S 3 、S 4 And the model adjusts the corresponding frequency, and finally, different tested areas are distinguished by adjusting the coupling coefficient.
As shown in FIG. 4, a coupling mode between different channels is also provided, and S is output from different channels by adjusting the coupling coefficient jx The coupling signals are coupled to different degrees, and the testees are distinguished.
The above embodiments are only illustrative of the preferred embodiments of the present application and are not intended to limit the scope of the present application, and various modifications and improvements made by those skilled in the art to the technical solutions of the present application should fall within the protection scope defined by the claims of the present application without departing from the design spirit of the present application.
Claims (4)
1. The multi-channel brain-electrical signal modeling method for the SSVEP brain-computer interface is characterized in that the method is used for stimulating the evoked brain-electrical signal through the SSVEP brain-computer interface to achieve the action of completely separating from the limbs, and the intelligent instrument is controlled only by brain ideas, and the method comprises the following steps:
the method for determining the SSVEP signal frequency range and the signal number of the tested person comprises the following steps: obtaining an electroencephalogram signal of a tested person through SSVEP stimulation induction; selecting a subject-required SSVEP frequency from the electroencephalogram signal; modulating the frequency corresponding to the SSVEP stimulation, and determining the SSVEP signal frequency range and the signal number suitable for controlling the brain-controlled intelligent instrument;
based on the signal numbers, a basic neuron population model is established for generating a plurality of rhythmic narrowband signals of the frequency range, and the method comprises: adjusting parameters of excitatory and inhibitory linear transformation functions using the basic neuron population model and producing the narrowband signals at different frequencies;
determining a multiple dynamic neuron population model for modulating a single channel SSVEP frequency signal suitable for a subject based on the frequency range having a plurality of the rhythmic narrowband signals, the method comprising: determining a desired combination of excitation and suppression parameters of the multiple dynamic neuron population model and its linear transfer function according to the subject's selected frequency in the frequency range, and modulating the single channel SSVEP frequency signal by adjusting the weights;
constructing a multi-channel multi-dynamic neuron group model based on the multi-dynamic neuron group model and the single-channel SSVEP frequency signals, and setting a coupling coefficient matrix, so as to construct multi-channel brain electrical signals by adjusting coupling coefficients among different basic neuron groups, and reflect the difference among testees; the specific method comprises the following steps: the multi-channel multi-dynamic neuron group model realizes the coupling of different intensities between brain-electrical signals with different frequencies by setting a coupling coefficient matrix, selects a channel facing an SSVEP brain-computer interface, adjusts the coupling coefficient between the channels through the multi-channel multi-dynamic neuron group model after the channel selection is completed, further modulates the brain-electrical signals of different subjects, and the difference is distinguished by adjusting the coupling coefficient.
2. The method for modeling the multichannel electroencephalogram signals facing the SSVEP brain-computer interface according to claim 1, wherein the multi-dynamic neuron group model limits the required electroencephalogram signal frequency through setting of weight coefficients.
3. The method of modeling a multichannel electroencephalogram signal for an SSVEP brain-computer interface according to claim 1, wherein the method of modulating the single-channel SSVEP frequency signal comprises: the relative proportion of the signals of the cortex area with different dynamics characteristics is adjusted by adjusting the weights and setting the excitation and inhibition parameters of a plurality of different linear conversion functions through the multi-dynamic neuron group model, so that the single-channel SSVEP frequency signals with richer frequencies and wider frequency bands are generated.
4. The method for modeling a multichannel electroencephalogram signal for an SSVEP brain-computer interface according to claim 1, wherein the parameters of the multichannel, multi-dynamic neuron population model include: average excitatory synaptic gain, average inhibitory synaptic gain, average time constant of excitatory membrane and average time delay of dendrite, average time constant of inhibitory membrane, average time delay of dendrite, average number of synaptic connections on excitatory feedback loop, average number of synaptic connections on inhibitory feedback loop, non-linear S-function parameter, several pairs of weight coefficients and coupling coefficient to other channels.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130142476A (en) * | 2012-06-19 | 2013-12-30 | 서울대학교산학협력단 | Brain wave analysis system using amplitude-modulated steady-state visual evoked potential visual stimulus |
| CN105022486A (en) * | 2015-07-17 | 2015-11-04 | 西安交通大学 | Electroencephalogram identification method based on different expression drivers |
| CN110413116A (en) * | 2019-07-24 | 2019-11-05 | 西安交通大学 | A kind of Steady State Visual Evoked Potential brain-computer interface design method based on FPGA |
| CN114521904A (en) * | 2022-01-25 | 2022-05-24 | 中山大学 | Electroencephalogram activity simulation method and system based on coupled neuron group |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US7269456B2 (en) * | 2002-05-30 | 2007-09-11 | Collura Thomas F | Repetitive visual stimulation to EEG neurofeedback protocols |
| WO2021026400A1 (en) * | 2019-08-06 | 2021-02-11 | Neuroenhancement Lab, LLC | System and method for communicating brain activity to an imaging device |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130142476A (en) * | 2012-06-19 | 2013-12-30 | 서울대학교산학협력단 | Brain wave analysis system using amplitude-modulated steady-state visual evoked potential visual stimulus |
| CN105022486A (en) * | 2015-07-17 | 2015-11-04 | 西安交通大学 | Electroencephalogram identification method based on different expression drivers |
| CN110413116A (en) * | 2019-07-24 | 2019-11-05 | 西安交通大学 | A kind of Steady State Visual Evoked Potential brain-computer interface design method based on FPGA |
| CN114521904A (en) * | 2022-01-25 | 2022-05-24 | 中山大学 | Electroencephalogram activity simulation method and system based on coupled neuron group |
Non-Patent Citations (2)
| Title |
|---|
| 多通道神经群模型建模及分析;崔冬 等;中国科学:信息科学;第41卷(第08期);第978-988页 * |
| 表情驱动下脑电信号的建模仿真及分类识别;张小栋 等;西安交通大学学报;第50卷(第06期);第1-8页 * |
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