An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm - Li, Rui; Zhang, Xiaodong; Lu, Zhufeng; Liu, Chang; Li, Hanzhe; Sheng, Weihua; Odekhe, Randolph (Frontiers in Neuroscience, 2018)
One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, eighteen subjects were involved in both offline and online experiments. The grand average accuracy over eighteen subjects was 81.31 ± 5.82% during the online experiment. The experimental results indi-cated that the proposed FE-BCI system achieved good performance and can be efficiently ap-plied for prosthesis control.