WPT-ANN and belief theory based EEG/EMG data fusion for movement identification

Abstract

The electromyography (EMG) and electroencephalography (EEG) are two frequently used modalities of bio-signals in the field of bio-robotics. However, it is insufficient to use a single isolated modality, due to the presence of artifacts, the lack of information, etc. To solve the problem, this paper proposes an EEG/EMG data fusion method that take advantages of both signals and overcome their drawbacks to achieve accurate identification of movements. The two types of bio-signals were preprocessed through wavelet packets transform (WPT) and classified by the artificial neural network (ANN). Then, the belief theory was introduced to allow for model uncertainty and imprecision, which adapts to the ambiguities and conflicts between sources. Experimental results show that the proposed EEG/EMG data fusion method outperformed the strategies based on only one modality. The research findings provide new impetus to bio-robotic applications.