Brain Computer Interface for Neurorehabilitation with Kinesthetic Feedback

Abstract

This study aims to record and translate the process of motor imagery to enable orthotic extension and flexion of the finger. This study probes the feasibility of developing a Brain Computer Interface system by prioritizing - Data Acquisition, Deep Learning, and End Effector of the system. The optimal channels to record MI data were deduced using Recursive Feature Elimination from a sixty four channel online dataset. Eight electrode channels of the OpenBCI Cyton kit were used, covering the sensorimotor cortex region of five subjects to record electroencephalographic data by following a standardized EEG acquisition protocol. Classification of tasks was carried out on a custom deep learning architecture using a convolutional layer and LSTM. The results were passed to an orthotic brace that provided a kinesthetic feedback mechanism to improve grip strength and support the neurorehabilitation of its user.