Creation of a high resolution EEG based Brain Computer Interface for classifying motor imagery of daily life activities

Abstract

Application of Brain Computer Interface (BCI) is revolutionizing control of prosthetic or exoskeleton devices directly through human thought. A BCI is expected to classify day-to-day life activities like grabbing and lifting a glass of water. Currently, motor imagery based BCI for two closely separated muscle groups like grabbing and lifting an object has not been studied. Challenge of classifying motor imagery of these activities accurately could be solved by using individual BCI. We proposed to achieve the same by using a neural network (machine learning) classifier on high resolution (129 channel) EEG data evaluated continuously every 80ms after spatial filtering using spherical Laplacian. This study employed a motor imagery based BCI optimized for individual subjects (n=28) using EEG data of actual movement for classifying motor imagery of grab, lift and grab+lift of right forearm. A three layered neural network with two output nodes was created for classifying the motor imagery using power of 8–14 Hz band of 500 ms EEG data. This BCI was able to classify motor imagery with 95.65% accuracy. In continuous evaluation, BCI showed a True Positive Rate of 24.89% and False Positive Rate of 12.93%. The percentage of correctly classified motor imagery in each trial was 84.99%, 72.23%, 17.07% for grab, lift and combined respectively. In conclusion, the current BCI was able to classify the motor imagery of grab, lift and grab+lift successfully based on EEG of movement data without any prior training of motor imagery based on last 500ms of data.