Abstract: Disclosed are an EEG signal generation network, method and storage medium. The EEG signal generation network includes a real EEG signal input end, a real EEG signal labeling module, a generator, a sharing module, a discriminator, and a classifier. The EEG signal generation network is configured to minimize losses of the generator, discriminator and classifier, and to minimize a combined loss of the discriminator and classifier through training, and to generate a new event-related potential.

Abstract: Disclosed are a method for identifying P300 signal based on MS-CNN, device and storage medium, the method includes: collecting P300 signal; denoising the collected P300 signal; establishing MS-CNN network and setting network parameters thereof; receiving cross-subject data and performing feature extraction and classification to establish a cross-subject model via the MS-CNN network; receiving subject-specific data and establishing a subject-specific model via the MS-CNN network, based on a transfer learning technology and the cross-subject model.

Abstract: Disclosed is a method for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue including: using independent component analysis and wavelet packet transformation to preprocess EEG data; constructing the preprocessed EEG data into a temporal brain network with dynamic characteristics based on a sliding window method; measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework; and performing statistical analysis on the spatiotemporal topology of the temporal brain network to obtain a correlation between behaviors related to driving fatigue and dynamic characteristics of the temporal brain network.

Abstract: Disclosed is a method for identifying driving fatigue based on a CNN-LSTM deep learning model including: collecting electroencephalograph signals of a subject during simulated driving; randomly issuing an operating command during simulated driving, and dividing the electroencephalograph signals into fatigue data and non-fatigue data according to a reaction time for the subject to complete the operating command; performing band-pass filtering and mean removal preprocessing on the electroencephalograph signals, and respectively extracting N minutes of fatigue electroencephalograph signal data and N minutes of non-fatigue electroencephalograph signal data to be detected; performing independent component analysis on the electroencephalograph signal data to remove interference signals; establishing a CNN-LSTM model and setting network parameters of the CNN-LSTM model; transmitting the electroencephalograph signal data with interference signals removed to a CNN network for feature extraction; and reshaping data of