Electroencephalogram (eeg) emotion recognition method based on spiking convolutional neural network

An electroencephalogram (EEG) emotion recognition method based on a spiking convolutional neural network, comprising the following steps:

• step 1:
• data set acquisition:
• acquiring EEG data of a subject when the subject watches movie clips, and immediately completing, by the subject, a questionnaire after watching each movie clip, to report an emotional response of the subject to each movie clip, wherein the emotional response comprises positive, neutral, and negative;
• and the EEG data is EEG signals that are acquired by a 10-20 EEG system and that are at electrode positions designated by 32 channels;
• step 2:
• data preprocessing:
• performing downsampling and ocular artifact removal preprocessing on an original EEG signal, filtering a time domain signal by using a Hanning window and performing fast Fourier transformation, performing window sliding by using a channel signal of each acquired electrode, and calculating differential entropy features of 32 channels at four frequency bands of all acquired electrodes;
• step 3:
• sample generation:
• performing non-overlapping window sliding on processed differential entropy features at T time windows, and performing the operation on each channel at each frequency band, to obtain time*channel*frequency band;
• converting a one-dimensional channel data sequence into a two-dimensional mesh matrix sequence, wherein a position correspondence is obtained through a two-dimensional topology map of an EEG electrode cap;
• and finally obtaining an input sample:
• time*H*W*frequency band, wherein H and W are a height and a width of the two-dimensional mesh matrix sequence;
• step 4:
• defining an input and an output of a model, wherein an input of single training of the model comprises a batch of samples, a structure of each sample is time*H*W*frequency band, and thus an input structure Input of the model is batch*time*H*W*frequency band;
• and an output of the model is a batch of vectors in a form of one-hot, and an output structure Output=batch*classes, wherein classes represent a probability that the sample belongs to the class, which is represented by using a decimal 0 to 1, and batch represents a quantity of batches;
• step 5:
• defining a spiking neuron, wherein
• a leaky-integrate-fire (LIF) model is used as a neuron model;
• and the LIF model is a spiking neural network combining a conventional CNN, a fully connected network, and a spiking neuron mechanism, and performs, by using a modified activation function and an activation function of an activation model of the spiking neuron with help of a surrogate gradient function, input and output between layers in the activation mode in a network training process, and also sequentially performs spiking output, activation, and transmission on an intra-layer time slice;
• step 6:
• defining a spiking neural network architecture, wherein
• a spiking neural network structure comprises an adaptive spiking convolutional encoder, a spiking convolutional feature extraction network, and a spiking fully connected classifier;
• step 7:
• defining a target function, wherein the target function used when the spiking neural network is trained is a mean square error function;
• and
• step 8:
• training and test:
• inputting a training set into the spiking neural network according to the input structure defined in step 3 for a plurality of rounds of training;
• and inputting a test set used for a trained model into the network in a same manner for prediction after each round of training, comparing a predicted result that is used as an output with a real label to calculate a mean square error, and finally obtaining overall classification accuracy of the spiking neural network on the test set.