CN117311516B - Motor imagery electroencephalogram channel selection method and system - Google Patents
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Abstract
The invention provides a motor imagery electroencephalogram channel selection method and a motor imagery electroencephalogram channel selection system, comprising the following steps: mapping motor imagery electroencephalogram data having a number of electroencephalogram channels to a NeuCube model that builds an SNN cube comprising input neurons and other neurons mapped by different electroencephalogram channels; analyzing the input neurons by adopting a clustering method, and grouping the neurons according to pulse communication of the neurons in the training process to obtain a neuron cluster taking the input neurons as the center; comparing the number of neurons in each neuron cluster with the total number of neurons in the model to obtain the proportion index of each input neuron; determining an average interaction between input neurons based on impulse communications of neurons of different clusters of neurons during training; and deleting the electroencephalogram channels corresponding to the input neurons with large proportion indexes and small average interaction, and selecting electroencephalogram data of other electroencephalogram channels to retrain and complete classification. The invention can effectively select the electroencephalogram channel and improve the classification accuracy.
Description
Technical Field
The invention relates to the technical field of brain-computer interfaces, in particular to a motor imagery electroencephalogram channel selection method and system.
Background
The Brain-computer interface (Brain-Computer Interface, BCI) is a man-machine interaction mode for realizing communication and control of human Brain and external electronic equipment based on Brain signals. Motor Imagery (MI) is a psychological process that imagines specific behaviors rather than actually performing those behaviors. BCI systems based on electroencephalogram (EEG) data generated by motor imagery can help establish a connection between the brain and external devices, can be used to maneuver, control unmanned aerial vehicles, help disabled or communication impaired patients resume control and communication functions, and like tasks.
While a large number of brain electrical channels can provide more background neural activity information, they also introduce redundancy and noise data, resulting in high-dimensional data. The channel selection method can be used for reducing irrelevant noise and redundant information, thereby reducing computational complexity, improving classification accuracy by avoiding overfitting, reducing setup time, and providing a BCI with better performance. However, the existing channel selection and classification technologies have the problems of high operation complexity and high energy consumption.
Disclosure of Invention
The invention provides a motor imagery electroencephalogram channel selection method and a motor imagery electroencephalogram channel selection system, which are used for solving the problems existing in the prior art, and the technical scheme provided by the invention is as follows:
in one aspect, a motor imagery electroencephalogram channel selection method is provided, the method comprising:
s1, acquiring and preprocessing motor imagery electroencephalogram data with a plurality of electroencephalogram channels;
s2, mapping the electroencephalogram data to a NeuCube model based on a pulse neural network, and training the NeuCube model, wherein the NeuCube model constructs a three-dimensional SNN cube which simulates a brain and comprises neurons mapped by different electroencephalogram channels and other neurons, and the neurons are mapped by different electroencephalogram channels
Is called an input neuron;
s3, analyzing the space-time relationship of the input neurons in the Neucube model by adopting a clustering method, and grouping the neurons in the model according to pulse communication of the neurons in the training process to obtain a neuron cluster taking the input neurons as a cluster center;
s4, comparing the number of neurons in each neuron cluster with the total number of neurons in the NeuCube model to obtain the proportion index of each input neuron;
s5, determining average interaction among all input neurons according to pulse communication of neurons of different neuron clusters in the training process;
and S6, deleting the electroencephalogram channels corresponding to the input neurons with large proportion indexes and small average interaction as unimportant electroencephalogram channels, selecting motor imagery electroencephalogram data of other undeleted electroencephalogram channels, retraining a NeuCube model, and completing classification.
Optionally, the NeuCube model includes: the device comprises a data coding module, a three-dimensional SNN cube module and an output module;
the data coding module is used for converting the input electroencephalogram data into pulse sequences of different electroencephalogram channels;
the three-dimensional SNN cube module is used for mapping pulse sequences of different brain electrical channels to neurons in the three-dimensional cube, adopting a small world network to perform initial connection among different neurons, randomly setting initial connection weights, training in an unsupervised mode after initializing the three-dimensional cube, and modifying the initial connection weights by utilizing a pulse time dependent plasticity rule;
the output module is used for recognizing the cube state by using the predefined mode category classification by adopting the supervision training after the non-supervision training stage.
Optionally, the step S3 specifically includes:
taking the input neurons as cluster centers of neuron clusters, and marking with input variables representing brain electrical channels;
the remaining neurons of a cluster of neurons are determined based on the number of pulses received from each cluster center, each neuron belonging to a particular cluster of neurons, with the number of pulses received from the cluster center of the particular cluster of neurons being the greatest compared to the cluster centers of other clusters of neurons.
Optionally, the step S5 specifically includes:
calculating the pulse communication total quantity of pulse communication between the neurons of each neuron cluster and the neurons of other neuron clusters in the training process;
the resulting value of the total amount of impulse traffic for each of the neuron clusters divided by the number of neurons for each of the neuron clusters is used as the average interaction between the input neurons for each of the neuron clusters.
Optionally, the unimportant electroencephalogram channel comprises an electroencephalogram channel corresponding to at least one input neuron with a large scale index and small average interactions.
In another aspect, there is provided a motor imagery electroencephalogram channel selection system, the system comprising:
the acquisition preprocessing module is used for acquiring and preprocessing motor imagery electroencephalogram data with a plurality of electroencephalogram channels;
the mapping training module is used for mapping the electroencephalogram data to a NeuCube model based on a pulse neural network and training the NeuCube model, and the NeuCube model is used for constructing a three-dimensional SNN cube which simulates a brain and comprises neurons mapped by different electroencephalogram channels and other neurons, wherein the neurons mapped by different electroencephalogram channels are called as input neurons;
the grouping module is used for analyzing the space-time relationship of the input neurons in the Neucube model by adopting a clustering method, and grouping the neurons in the model according to the pulse communication level of the neurons in the training process to obtain a neuron cluster taking the input neurons as a cluster center;
the comparison module is used for comparing the number of neurons in each neuron cluster with the total number of neurons in the NeuCube model to obtain the proportion index of each input neuron;
a determining module for determining an average interaction between the input neurons according to impulse communication of neurons of different neuron clusters during training;
and the selection module is used for deleting the electroencephalogram channels corresponding to the input neurons with large proportion indexes and small average interaction as unimportant electroencephalogram channels, selecting motor imagery electroencephalogram data of other undeleted electroencephalogram channels, retraining the NeuCube model, and finishing classification.
Optionally, the NeuCube model includes: the device comprises a data coding module, a three-dimensional SNN cube module and an output module;
the data coding module is used for converting the input electroencephalogram data into pulse sequences of different electroencephalogram channels;
the three-dimensional SNN cube module is used for mapping pulse sequences of different brain electrical channels to neurons in the three-dimensional cube, adopting a small world network to perform initial connection among different neurons, randomly setting initial connection weights, training in an unsupervised mode after initializing the three-dimensional cube, and modifying the initial connection weights by utilizing a pulse time dependent plasticity rule;
the output module is used for recognizing the cube state by using the predefined mode category classification by adopting the supervision training after the non-supervision training stage.
Optionally, the grouping module is specifically configured to:
taking the input neurons as cluster centers of neuron clusters, and marking with input variables representing brain electrical channels;
the remaining neurons of a cluster of neurons are determined based on the number of pulses received from each cluster center, each neuron belonging to a particular cluster of neurons, with the number of pulses received from the cluster center of the particular cluster of neurons being the greatest compared to the cluster centers of other clusters of neurons.
Optionally, the determining module is specifically configured to:
calculating the pulse communication total quantity of pulse communication between the neurons of each neuron cluster and the neurons of other neuron clusters in the training process;
the resulting value of the total amount of impulse traffic for each of the neuron clusters divided by the number of neurons for each of the neuron clusters is used as the average interaction between the input neurons for each of the neuron clusters.
Optionally, the unimportant electroencephalogram channel comprises an electroencephalogram channel corresponding to at least one input neuron with a large scale index and small interactions.
In another aspect, an electronic device is provided, the electronic device including a processor and a memory, the memory having instructions stored therein, the instructions being loaded and executed by the processor to implement the motor imagery electroencephalogram channel selection method described above.
In another aspect, a computer-readable storage medium having instructions stored therein that are loaded and executed by a processor to implement the motor imagery electroencephalogram channel selection method described above is provided.
Compared with the prior art, the technical scheme has at least the following beneficial effects:
according to the invention, a low-energy architecture based on a pulse neural network is adopted to classify a motor imagery small data set, in the classifying process, the time and space information of the electroencephalogram signals are considered, the interaction information between the neuron cluster occupancy rate and the neurons corresponding to different electroencephalogram channels is calculated, the unimportant electroencephalogram channels are selected to be deleted, then the NeuCube model is retrained and classified, and the electroencephalogram channels can be selected while learning and understanding the electroencephalogram signals, so that the energy consumption is reduced, the overfitting is avoided, the classifying precision is improved, and the practical application performance of a BCI system is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a motor imagery electroencephalogram channel selection method provided by an embodiment of the invention;
FIG. 2 is a structure diagram of a NeuCube model provided by an embodiment of the invention;
FIG. 3 is a diagram of a neuron cube according to an embodiment of the invention;
FIG. 4 is a diagram of initialized neuronal cube network connections according to an embodiment of the present invention;
FIG. 5 is a diagram of a cube connection after training in an unsupervised manner, provided by an embodiment of the present invention;
fig. 6 is a graph of a result after clustering neurons corresponding to twenty-two brain electrical channels according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the percentage of the number of neurons in each neuron cluster to the total number of neurons in the NeuCube model according to an embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating the average interactions between input neurons of each of the neuron clusters provided by an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a motor imagery electroencephalogram channel selection method, which includes:
s1, acquiring and preprocessing motor imagery electroencephalogram data with a plurality of electroencephalogram channels;
s2, mapping the electroencephalogram data to a NeuCube model based on a pulse neural network, and training the NeuCube model, wherein the NeuCube model constructs a three-dimensional SNN cube which simulates a brain and comprises neurons mapped by different electroencephalogram channels and other neurons, and the neurons mapped by different electroencephalogram channels are called as input neurons;
s3, analyzing the space-time relationship of the input neurons in the Neucube model by adopting a clustering method, and grouping the neurons in the model according to pulse communication of the neurons in the training process to obtain a neuron cluster taking the input neurons as a cluster center;
s4, comparing the number of neurons in each neuron cluster with the total number of neurons in the NeuCube model to obtain the proportion index of each input neuron;
s5, determining average interaction among all input neurons according to pulse communication of neurons of different neuron clusters in the training process;
and S6, deleting the electroencephalogram channels corresponding to the input neurons with large proportion indexes and small average interaction as unimportant electroencephalogram channels, selecting motor imagery electroencephalogram data of other undeleted electroencephalogram channels, retraining a NeuCube model, and completing classification.
The following describes in detail a motor imagery electroencephalogram channel selection method provided by an embodiment of the present invention with reference to fig. 2 to 8, including:
s1, acquiring and preprocessing motor imagery electroencephalogram data with a plurality of electroencephalogram channels;
the embodiment of the invention adopts a Butterworth band-pass filter to screen the motor imagery electroencephalogram data in a certain frequency range, and intercepts the data with fixed time length in the middle of the data for training and classification. The preferred scheme is that a 6-order Butterworth band-pass filter is adopted to screen motor imagery electroencephalogram data in the frequency range of 8-30Hz, and data in the middle of the data (motor imagery of a tested person) are intercepted for training and classification.
The electroencephalogram data has 22 electroencephalogram channels, namely 22 electrodes are arranged on electrode caps worn by testees, 22 different electroencephalogram data (space-time data) with time space information are collected, and all data in the finally obtained data set are in the format of:wherein the processed dataset comprises 9 folders with 576 csv files under each folder, i.e. 576 samples, according to 7:3 separate, 403 for training and 173 for testing, within each. Csv file isFor each value, the following table describes:
s2, mapping the electroencephalogram data to a NeuCube model based on a pulse neural network, and training the NeuCube model, wherein the NeuCube model constructs a three-dimensional SNN cube which simulates a brain and comprises neurons mapped by different electroencephalogram channels and other neurons, and the neurons mapped by different electroencephalogram channels are called as input neurons;
optionally, as shown in fig. 2, the NeuCube model includes: the device comprises a data coding module, a three-dimensional SNN cube module and an output module;
the data coding module is used for converting the input electroencephalogram data into pulse sequences of different electroencephalogram channels;
specifically, the embodiment of the invention adopts a SF (step forward) coding algorithm to convert the input electroencephalogram data into pulse sequences of different electroencephalogram channels, the floating point number in metadata is similarly converted into data only containing 0 and 1, and then the pulse sequences are imported into a three-dimensional SNN cube of NeuCube for processing and classification.
The three-dimensional SNN cube module is configured to map pulse sequences of the different brain electrical channels to neurons in the three-dimensional cube, and the different neurons are initially connected by using a small world network, where the small world network neurons in the embodiment of the invention are connected within a certain radius (for example, r=3), and on the basis of mapping of the international 10-5 positioning system, a structure composed of 346 neurons is designed, and all 22 brain electrical channels are mapped to the cube, as shown in fig. 3, the 22 input neurons include: FZ, POz, P2, FC3, etc., are indicated by dots in fig. 3, and other neurons are indicated by star points.
The initial connection weights are set randomly, the initial three-dimensional cube can be regarded as a liquid machine (Liquid state machines, LSM) which is composed of a large number of single neurons which are randomly interconnected to form an LSM network, the initialized network connection is shown in fig. 4, wherein the brightness of the neuron points represents the initial activation intensity of the initial connection weights, and the connection lines among the neurons represent the random initial connection weights.
After initializing the three-dimensional cube, training is performed in an unsupervised manner, using pulse-time dependent plasticity (STDP) rules to modify initial connection weights, and the cube connections after training in an unsupervised manner are shown in fig. 5, where the brightness of the neuron points represents the intensity of their activation after training, and the links between neurons represent the connection weights after training, including excitatory and inhibitory connections.
The output module is used for recognizing the cube state by using the predefined mode category classification by adopting the supervision training after the non-supervision training stage.
S3, analyzing the space-time relationship of the input neurons in the Neucube model by adopting a clustering method, and grouping the neurons in the model according to pulse communication of the neurons in the training process to obtain a neuron cluster taking the input neurons as a cluster center;
optionally, the step S3 specifically includes:
taking the input neurons as cluster centers of neuron clusters, and marking with input variables representing brain electrical channels;
the remaining neurons of a cluster of neurons are determined based on the number of pulses received from each cluster center, each neuron belonging to a particular cluster of neurons, with the number of pulses received from the cluster center of the particular cluster of neurons being the greatest compared to the cluster centers of other clusters of neurons.
The result of clustering the neurons corresponding to twenty-two brain electrical channels is shown in fig. 6.
S4, comparing the number of neurons in each neuron cluster with the total number of neurons in the NeuCube model to obtain the proportion index of each input neuron;
the embodiment of the invention designs a proportional index, and the calculation formula of the index is as follows: the percentage of the number of neurons in each cluster to the total number of neurons in the NeuCube model is shown in fig. 7 (test subject No. 3), where the input neurons FZ and POz have the largest cluster proportions of 26.1% and 25.8%, respectively. This suggests that they are two brain electrical channels that require attention.
S5, determining average interaction among all input neurons according to pulse communication of neurons of different neuron clusters in the training process;
optionally, the step S5 specifically includes:
calculating the pulse communication total quantity of pulse communication between the neurons of each neuron cluster and the neurons of other neuron clusters in the training process;
specifically, a pulse is sent between two neurons, that is, a communication is generated, and how many times the neurons of each neuron cluster and the neurons of other neuron clusters share in the training process are counted.
The resulting value of the total amount of impulse traffic for each of the neuron clusters divided by the number of neurons for each of the neuron clusters is used as the average interaction between the input neurons for each of the neuron clusters.
The average interactions between the input neurons of each neuron cluster are shown in fig. 8, wherein the thickness of the line represents the intensity between the input neurons of each neuron cluster in the unsupervised learning stage, the thicker the line is, the larger the average interactions are, the thinner the line is, and the smaller the average interactions are, and it can be seen that the input neurons with the largest proportion index, namely FZ and POz, have the smallest average interactions of the electroencephalogram channels corresponding to the other input neurons.
And S6, deleting the electroencephalogram channels corresponding to the input neurons with large proportion indexes and small average interaction as unimportant electroencephalogram channels, selecting motor imagery electroencephalogram data of other undeleted electroencephalogram channels, retraining a NeuCube model, and completing classification.
Optionally, the unimportant electroencephalogram channel comprises an electroencephalogram channel corresponding to at least one input neuron with a large scale index and small average interactions.
In the embodiment of the invention, FZ and POz are input neurons with large proportion index and small average interaction, the size and the size are not absolute, and at least one of the numerical values in the front can be selected according to the requirement.
The embodiment of the invention does not select one of the proportional index or the average interaction as the selection basis, but combines the results of the proportional index and the average interaction to select the unimportant electroencephalogram channel more accurately.
The embodiment of the invention also provides a motor imagery electroencephalogram channel selection system, which comprises:
the acquisition preprocessing module is used for acquiring and preprocessing motor imagery electroencephalogram data with a plurality of electroencephalogram channels;
the mapping training module is used for mapping the electroencephalogram data to a NeuCube model based on a pulse neural network and training the NeuCube model, and the NeuCube model is used for constructing a three-dimensional SNN cube which simulates a brain and comprises neurons mapped by different electroencephalogram channels and other neurons, wherein the neurons mapped by different electroencephalogram channels are called as input neurons;
the grouping module is used for analyzing the space-time relationship of the input neurons in the Neucube model by adopting a clustering method, and grouping the neurons in the model according to the pulse communication level of the neurons in the training process to obtain a neuron cluster taking the input neurons as a cluster center;
the comparison module is used for comparing the number of neurons in each neuron cluster with the total number of neurons in the NeuCube model to obtain the proportion index of each input neuron;
a determining module for determining an average interaction between the input neurons according to impulse communication of neurons of different neuron clusters during training;
and the selection module is used for deleting the electroencephalogram channels corresponding to the input neurons with large proportion indexes and small average interaction as unimportant electroencephalogram channels, selecting motor imagery electroencephalogram data of other undeleted electroencephalogram channels, retraining the NeuCube model, and finishing classification.
Optionally, the NeuCube model includes: the device comprises a data coding module, a three-dimensional SNN cube module and an output module;
the data coding module is used for converting the input electroencephalogram data into pulse sequences of different electroencephalogram channels;
the three-dimensional SNN cube module is used for mapping pulse sequences of different brain electrical channels to neurons in the three-dimensional cube, adopting a small world network to perform initial connection among different neurons, randomly setting initial connection weights, training in an unsupervised mode after initializing the three-dimensional cube, and modifying the initial connection weights by utilizing a pulse time dependent plasticity rule;
the output module is used for recognizing the cube state by using the predefined mode category classification by adopting the supervision training after the non-supervision training stage.
Optionally, the grouping module is specifically configured to:
taking the input neurons as cluster centers of neuron clusters, and marking with input variables representing brain electrical channels;
the remaining neurons of a cluster of neurons are determined based on the number of pulses received from each cluster center, each neuron belonging to a particular cluster of neurons, with the number of pulses received from the cluster center of the particular cluster of neurons being the greatest compared to the cluster centers of other clusters of neurons.
Optionally, the determining module is specifically configured to:
calculating the pulse communication total quantity of pulse communication between the neurons of each neuron cluster and the neurons of other neuron clusters in the training process;
the resulting value of the total amount of impulse traffic for each of the neuron clusters divided by the number of neurons for each of the neuron clusters is used as the average interaction between the input neurons for each of the neuron clusters.
Optionally, the unimportant electroencephalogram channel comprises an electroencephalogram channel corresponding to at least one input neuron with a large scale index and small interactions.
The specific functional structure of the motor imagery electroencephalogram channel selection system provided by the embodiment of the invention corresponds to the motor imagery electroencephalogram channel selection method provided by the embodiment of the invention, and the detailed description is omitted.
Fig. 9 is a schematic structural diagram of an electronic device 900 according to an embodiment of the present invention, where the electronic device 900 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 901 and one or more memories 902, where the memories 902 store instructions, and the instructions are loaded and executed by the processors 901 to implement the steps of the motor imagery electroencephalogram channel selection method described above.
In an exemplary embodiment, a computer readable storage medium, such as a memory including instructions executable by a processor in a terminal to perform the motor imagery electroencephalogram channel selection method described above, is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A motor imagery electroencephalogram channel selection method, characterized in that the method comprises:
s1, acquiring and preprocessing motor imagery electroencephalogram data with a plurality of electroencephalogram channels;
s2, mapping the electroencephalogram data to a NeuCube model based on a pulse neural network, and training the NeuCube model, wherein the NeuCube model constructs a three-dimensional SNN cube which simulates a brain and comprises neurons mapped by different electroencephalogram channels and other neurons, and the neurons mapped by different electroencephalogram channels are called as input neurons;
s3, analyzing the space-time relationship of the input neurons in the Neucube model by adopting a clustering method, and grouping the neurons in the model according to pulse communication of the neurons in the training process to obtain a neuron cluster taking the input neurons as a cluster center;
s4, comparing the number of neurons in each neuron cluster with the total number of neurons in the NeuCube model to obtain the proportion index of each input neuron;
s5, determining average interaction among all input neurons according to pulse communication of neurons of different neuron clusters in the training process;
s6, deleting an electroencephalogram channel corresponding to an input neuron with a large proportion index and small average interaction as an unimportant electroencephalogram channel, and selecting motor imagery electroencephalogram data of other undeleted electroencephalogram channels to retrain a NeuCube model and finish classification;
the NeuCube model comprises: the device comprises a data coding module, a three-dimensional SNN cube module and an output module;
the data coding module is used for converting the input electroencephalogram data into pulse sequences of different electroencephalogram channels;
the three-dimensional SNN cube module is used for mapping pulse sequences of different brain electrical channels to neurons in the three-dimensional cube, adopting a small world network to perform initial connection among different neurons, randomly setting initial connection weights, training in an unsupervised mode after initializing the three-dimensional cube, and modifying the initial connection weights by utilizing a pulse time dependent plasticity rule;
the output module is used for recognizing the cube state by using the predefined mode category classification by adopting the supervision training after the non-supervision training stage.
2. The method according to claim 1, wherein S3 specifically comprises:
taking the input neurons as cluster centers of neuron clusters, and marking with input variables representing brain electrical channels;
the remaining neurons of a cluster of neurons are determined based on the number of pulses received from each cluster center, each neuron belonging to a particular cluster of neurons, with the number of pulses received from the cluster center of the particular cluster of neurons being the greatest compared to the cluster centers of other clusters of neurons.
3. The method according to claim 1, wherein S5 specifically comprises:
calculating the pulse communication total quantity of pulse communication between the neurons of each neuron cluster and the neurons of other neuron clusters in the training process;
the resulting value of the total amount of impulse traffic for each of the neuron clusters divided by the number of neurons for each of the neuron clusters is used as the average interaction between the input neurons for each of the neuron clusters.
4. A motor imagery electroencephalogram channel selection system, the system comprising:
the acquisition preprocessing module is used for acquiring and preprocessing motor imagery electroencephalogram data with a plurality of electroencephalogram channels;
the mapping training module is used for mapping the electroencephalogram data to a NeuCube model based on a pulse neural network and training the NeuCube model, and the NeuCube model is used for constructing a three-dimensional SNN cube which simulates a brain and comprises neurons mapped by different electroencephalogram channels and other neurons, wherein the neurons mapped by different electroencephalogram channels are called as input neurons;
the grouping module is used for analyzing the space-time relationship of the input neurons in the Neucube model by adopting a clustering method, and grouping the neurons in the model according to the pulse communication level of the neurons in the training process to obtain a neuron cluster taking the input neurons as a cluster center;
the comparison module is used for comparing the number of neurons in each neuron cluster with the total number of neurons in the NeuCube model to obtain the proportion index of each input neuron;
a determining module for determining an average interaction between the input neurons according to impulse communication of neurons of different neuron clusters during training;
the selection module is used for deleting the electroencephalogram channels corresponding to the input neurons with large proportion indexes and small average interaction as unimportant electroencephalogram channels, selecting motor imagery electroencephalogram data of other undeleted electroencephalogram channels, retraining a Neucube model, and finishing classification;
the NeuCube model comprises: the device comprises a data coding module, a three-dimensional SNN cube module and an output module;
the data coding module is used for converting the input electroencephalogram data into pulse sequences of different electroencephalogram channels;
the three-dimensional SNN cube module is used for mapping pulse sequences of different brain electrical channels to neurons in the three-dimensional cube, adopting a small world network to perform initial connection among different neurons, randomly setting initial connection weights, training in an unsupervised mode after initializing the three-dimensional cube, and modifying the initial connection weights by utilizing a pulse time dependent plasticity rule;
the output module is used for recognizing the cube state by using the predefined mode category classification by adopting the supervision training after the non-supervision training stage.
5. The system according to claim 4, wherein the grouping module is specifically configured to:
taking the input neurons as cluster centers of neuron clusters, and marking with input variables representing brain electrical channels;
the remaining neurons of a cluster of neurons are determined based on the number of pulses received from each cluster center, each neuron belonging to a particular cluster of neurons, with the number of pulses received from the cluster center of the particular cluster of neurons being the greatest compared to the cluster centers of other clusters of neurons.
6. The system according to claim 4, wherein the determining module is specifically configured to:
calculating the pulse communication total quantity of pulse communication between the neurons of each neuron cluster and the neurons of other neuron clusters in the training process;
the resulting value of the total amount of impulse traffic for each of the neuron clusters divided by the number of neurons for each of the neuron clusters is used as the average interaction between the input neurons for each of the neuron clusters.
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