Abstract: A first device is logged in with a first user account. The first device displays an avatar of a second user account in a first user interface of the first device. In response to an interaction instruction triggered on the avatar of the second user account, the first device generates an interaction message according to a first field corresponding to the first user account, an action description field corresponding to the interaction instruction, and a second field corresponding to the second user account. The first device transmits the interaction message to a second device that is logged in with the second user account.

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