Mental fatigue recognition algorithm based on cascade forest and multi-modal fusion

Mental fatigue is an important factor affecting human cognitive function and work efficiency, but there is no publicly available multi-modal fusion database related to mental fatigue, and the EEG signals commonly used to identify mental fatigue are prone to burden and activity limitation during the acquisition process, which led to the proposal of a mental fatigue identification algorithm based on multi-modal physiological signals. The experiment used a continuous cognitive task to induce mental fatigue in the subjects, and two physiological signals, EEG and ECG, were acquired simultaneously. The 4-lead (Fp1, F7, F8, Fp2) EEG and ECG signals were used to construct the multi-modal fusion features, and inputted into the cascade forest model to complete the mental fatigue recognition task. Finally, 14 valid mental fatigue multi-modal datasets were obtained and an average recognition rate of 99.60% was achieved. By introducing the cascade forest and multi-modal fusion technology, the accuracy and robustness of mental fatigue recognition are effectively improved, which provides technical support for mental fatigue monitoring and intervention.