EEG signal classification based on improved variational mode decomposition and deep forest

The study of EEG signals is of great significance for the diagnosis and prevention of brain disease. Most of the previous studies are based on the binary classification of nonictal and ictal EEG signals, and there are few studies on the detailed division of EEG signals. In this paper, in addition to the binary classification of EEG signals, the multiclassification of EEG signals is also studied. An EEG signals recognition framework based on improved variational mode decomposition (VMD) and deep forest is proposed. Firstly, the L1 penalty term is introduced into the variational problem of VMD to improve the Tikhonov regularization term. The improved VMD algorithm is used to decompose the original signal. Second, a weighted minimum redundancy maximum relevance criterion is constructed for feature selection. Finally, a deep forest model is built to classify EEG signals. The feasibility of the proposed method is verified by EEG data from Bonn University and the Centre for Neurology and Sleep, Hauz Khas, New Delhi. The experimental results are compared with the traditional machine learning methods and the existing methods. Experimental results show that this method can recognize epileptic EEG signals effectively.