Improvement of Classification Accuracy of Four-Class Voluntary-Imagery fNIRS Signals using Convolutional Neural Networks

Abstract

Multiclass functional Near-Infrared Spectroscopy (fNIRS) signal classification has become a convenient way for optical brain-computer interface. fNIRS signal classification with high accuracy is a challenging assignment while the signals are produced by means of voluntary and imagery movements of the same limb. Since the activation in time and space of voluntary and imagery movement show a similar pattern, the classification accuracy by the conventional shallow classifiers cannot reach an acceptable range. This paper proposes an accuracy improvement approach with the use of Convolutional Neural Networks (CNNs). In this work, voluntary and imagery hand movements (left hand and right hand) were performed by several participants. These four-class signals were acquired utilizing fNIRS devices. The signals were separated based on the tasks and filtered. With manual feature extraction, the signals were classified by support vector machine and linear discriminant analysis. The automatic feature extraction and classification mechanism of the CNN were applied to the fNIRS signals. From the results, it was found that CNN improves the classification accuracy to an acceptable range, which has not been achieved by any convolutional network.