An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm

• Main Authors: Rui Li, Xiaodong Zhang, Zhufeng Lu, Chang Liu, Hanzhe Li, Weihua Sheng, Randolph Odekhe
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-12-01
• Series: Frontiers in Neuroscience
• Subjects: facial expressions; electroencephalography (EEG); brain computer interface (BCI); brain-controlled prosthesis; the motor cortex; the prefrontal cortex
• Online Access: https://www.frontiersin.org/article/10.3389/fnins.2018.00943/full

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, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control.