Multiscale fuzzy entropy based on local mean decomposition and Fisher rule for EEG feature extraction in human motion analysis

Electroencephalogram (EEG) is a nonlinear, non-stationary, and random weak signal generated by a large number of neurons. It has great research value and practical significance in artificial intelligence, biomedical engineering and other fields. EEG feature extraction is an important step which directly affects the processing results. Currently, the commonly used methods for EEG feature extraction include frequency domain or time domain analysis and time-frequency combination. Due to the nonlinearity of EEG, the above methods have certain limitations. Therefore, this paper proposes a multiscale fuzzy entropy based on local mean decomposition and Fisher rule for EEG feature extraction in human motion analysis. Firstly, the EEG signal is decomposed adaptively into a series of product function (PF) components. Then the effective PF component is selected and the multiscale fuzzy entropy is calculated. Multi-scale fuzzy entropy is used for feature extraction. Fisher rule is used to rank the feature classification ability of fuzzy entropy at different scales, and the multi-scale fuzzy entropy with the highest ranking is selected to form the optimal feature vector to achieve feature dimension reduction. Experimental results show that this proposed method can extract the features of EEG signal effectively, which verifies the validity and feasibility of the new method.