Affiliations: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Correspondence:
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Corresponding author. Shaoyang Hua, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. E-mail: hsy_study@163.com.
Abstract: Neural decoding is a technology to analyze intentions produced by neural activities, which has important applications in military, medical, entertainment and so on. As a typical application, decoding electromyogram (EMG) signals into corresponding gestures is an important content. In order to improve the accuracy of EMG signals recognition, researchers often extract effective features from EMG signals and classify gestures by constructing a reasonable classifier. However, because of the stochasticity of the signals, this method is not robust enough. This paper proposes a convolutional neural network (CNN) based on feature fusion, which can automatically learn and classify features from time-domain(TD) and frequency-domain(FD). To make full use of information, two fusion methods are used and compared. Experiments show that the proposed fusion methods are superior to the traditional algorithm for both normal people and amputees, and have better performance compared with CNN method using only one kind of information.
