A CNN-LSTM deep learning model-based driver fatigue identification method comprising the following steps: acquiring EEG signals from a subject under test during a driving simulation; issuing an operation instruction randomly during the driving simulation, and dividing, according to a response time of the test subject for completing the operation instruction, the EEG signals into fatigue data and non-fatigue data; performing band-pass filtering and mean removal preprocessing on the EEG signals, and extracting N minutes of the required fatigue EEG signal data and non-fatigue EEG signal data ; performing independent component analysis on the EEG signal data so as to remove interference signals therefrom; establishing a CNN-LSTM model, and configuring a network parameter of the CNN-LSTM model; inputting the interference-free EEG signal data into a CNN network and performing feature extraction; and reconstructing feature extraction data, and inputting the same into an LSTM network and performing classification. Experiment results indicate an improved accuracy of 96.3 ± 3.1% (grand mean ± population standard deviation).
CNN-LSTM DEEP LEARNING MODEL-BASED DRIVER FATIGUE IDENTIFICATION METHOD