Visualized Evidences for Detecting Novelty in Myoelectric Pattern Recognition using 3D Convolutional Neural Networks.

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Although myoelectric pattern recognition (MPR) has been considered as a milestone technique to enable dexterous control of multiple degrees of freedom, outlier data interference (i.e., novelty) is a big issue affecting stability of conventional MPR control and its wide applications. Inspired by video classification techniques, we propose a novel method using 3-dimensional (3D) convolutional neural network (CNN) for extracting sufficient spatial-temporal features from high-density surface electromyogram (EMG) recordings processed as a video stream where time-varying information of muscular activity was taken into account. Given the targeted task patterns accurately characterized by the proposed method, it is straightforward to discriminate and then reject outlier data interferences regarded as untargeted and unlearnt patterns. Meanwhile, the strength of the 3D CNN in discriminating targeted task patterns through spatial-temporal features is further visualized and confirmed by t-Distributed Stochastic Neighbor Embedding (t-SNE). The performance of the proposed method was evaluated with surface EMG recorded by two 6 × 8 electrode arrays placed over forearm flexors and extensors of 3 subjects performing 7 targeted and 4 outlier motions. The visualized results by t-SNE showed the targeted task patterns were separated with each concentrated into a small region, while the outlier patterns were scattered around. On this basis, a traditional Mahalanobis Distance (MD) based method was applied for novelty detection and targeted task classification. Finally, the proposed method yielded averaged error rate of 10.98% for the targeted task patterns and <; 5% for all outlier data, respectively, which outperformed a common baseline method. All these findings demonstrate the advantage of characterizing myoelectric patterns using the proposed method and the potential of applying it to reject outlier data interference.