Hybrid brain-computer interfacing paradigm for assistive robotics

Abstract

Human-computer interfacing, which can be controlled by eye movements and brain signals, is of widespread use in recent times as an intelligent rehabilitation aid. Although some exciting literature exists on robot-assisted physical therapy, enhancing the quality of neurological rehabilitation, most can only focus on a smaller range of control commands required for real-time robot navigation. In this paper, a hybrid brain-computer interfacing system to control a robotic arm has been proposed where an Electrooculography (EOG) and an Electroencephalography (EEG) sensor, respectively, have been used to select the joints of the robot and to control the movement of the selected joint in the required direction. The proposed technique, which applies interquartile range-based data augmentation to extracted time-domain features, can effectively deal with the outliers and prevent overfitting. Afterwards, a novel variant of the Two-Dimensional Convolutional Neural Network is employed for the classification of EOG signals. On the other hand, a Phase sensitive Common Spatial Pattern induced Linear Discriminant Analysis is utilized for classifying the EEG data. The classifiers exhibit satisfactory performance with 98.45 % and 96.61 % accuracy for EOG and EEG, respectively, leading to the implementation of an online robot navigation system in real-time. The system integrates EEG-based signals into the robotic control loop, enabling real-time error detection in the end-effector trajectory of the Robotic arm through Error Related Potential signals and confirming task completion or target attainment via P300 detection. The proposed framework yields an average steady-state error, peak overshoot, and settling time of 0.036, 2.5 %, and 30 s, respectively. Moreover, the average target reaching rate is 95 %, making it a suitable choice for real-time rehabilitative platforms in prosthetics design.