Disclosed is a training method for an electroencephalography mode classification model, applicable in tasks for personal identification (PI) and fatigue state while driving detection, comprising: acquiring, preprocessing, and marking electroencephalography (EEG) data to produce a training dataset having markers; inputting each piece of the EEG data into an attention mechanism-based convolutional neural network, extracting to produce a mode feature; and correcting, on the basis of the mode feature and markers of the EEG data, a parameter for use in an electroencephalography classification model. Also disclosed are a corresponding method for electroencephalography mode classification and a system for electroencephalography mode classification. Used in the classification of multiple driving-related tasks, such as the tasks for personal identification (PI) and fatigue state while driving detection, the average classification precision is high, a great balance is strived between classification precision and time costs, and potential application values are provided in the classification of multiple tasks of biomedical signals.
TRAINING METHOD FOR ELECTROENCEPHALOGRAPHY MODE CLASSIFICATION MODEL, CLASSIFICATION METHOD AND SYSTEM