Brain Computer Interface Based on Motor Imagery for Mechanical Arm Grasp Control

This paper puts forward a brain computer interface (BCI) system to realize the hand and wrist control using the Asea Brown Boveri (ABB) Mechanical Arm. This BCI system gathers four kinds of motor imaginary (MI) tasks (hand grasp, hand spread, wrist flexion and wrist extension) electroencephalogram (EEG) signals from 30 electrodes. It utilizes two fifth-order Butterworth Band-Pass Filter (BPF) with different bandwidths and normalization method to achieve the raw MI tasks EEG signals preprocessing. The main challenge of feature extraction is to analyze the MI task intention from the preprocessed EEG signals. Therefore, the proposed BCI system extracts eleven kinds of features in time domain and time-frequency domain and uses mutual information method to reduce the large dimension of the extracted features. In addition, the BCI system applies a single convolutional layer Convolutional neural networks (CNN) with 30 filters to implement the quaternary classification of MI tasks. Compared with existing research, the classification accuracy of this BCI system is increased by about 32%-35%. The actual mechanical arm grasping control experiments verifies that this BCI system has good adaptability.