Fatigue EEG Classification Study Based on Convolutionally Constrained Boltzmann Machine

As the demand for the detection of brain fatigue state grows, portable EEG instruments are becoming more and more critical in related research. In this study, we designed a 1-back task paradigm to induce fatigue EEG signals using the EmotivEpoc+14 EEG instrument. The EEG dataset was built based on the subjective rating scale rating scores of the KSS scale and the behavioral analysis results. Construct an improved convolutionally constrained Boltzmann machine model, introducing convolutional operations to the visible and hidden layers to achieve weight sharing. Feature selection using principal component analysis method combined with Pearson's coefficient to retain highly correlated features. Self-training-semi-supervised learning method is used to train the model, and the results show that the features extracted from the C-RBM model achieve 89% and 91% classification accuracy in the frontal and occipital lobes. After reducing the channels it is used for SVM and RF classifiers with the best results, SVM achieves 93% classification accuracy of HM + PSD + PE for occipital lobe and RF achieves 92% classification accuracy of HM + PSD + WE for occipital lobe. This shows that the combination of the C-RBM model proposed in this paper with SVM and RF classifiers can effectively use the reduced dimensionality channel features for fatigue detection, which provides a reference for fatigue detection of feature combinations for sparse channels.