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An intracortical activity decoding algorithm based on convolutional neural network (CNN) is proposed for classifying the primate intracortical activity of motor imagery under a series of movement tasks. An intracortical brain-computer interface allows the subjects to continuously drive a mobile robot using its brain activity from the motor cortex. Under the leave-one-day-out cross validation, the CNN-based algorithm achieves reliable classification performance in all the four movement tasks, including, Forward, Left, Right, and Stop. Experimental results indicate that the Cohen’s kappa of the two subjects are both above 0.4. Additionally, the comparison shows that the CNN-based method is significantly better than the linear discriminant analysis (LDA) which is a state-of-the-art decoding approach.

Intracortical Activity decoding of motor imagery based on Deep Convolutional Neural Network - a Pilot Study.