Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Abstract

This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with a shared control scheme. In general, subjects wearing a single-channel EEG electrode located at F8 (10–20 international standards) were required to perform mental tasks by mentally visualising the rotation of a star and mind relaxation at a specific time and by robot orientation. The visualisation of a rotating star suggests that the mobile robot is currently oriented towards a target, thus enabling target selection. We showed that proposed classifier obtained the best accuracy of 92.09% in classifying the subject’s performing mental rotation task or mental relaxation when compared with conventional classification methods such as support vector machine—75.69%, Kth-nearest neighbour—65.50% and linear discriminant analysis—65.20%. Furthermore, different from conventional methods, the use of 1D-CNN enables end-to-end learning, that is the automatic decoding of EEG signals without requiring feature selection or extraction. To validate that the proposed classifier performs better than conventional methods, the extracted kernel weights of proposed 1D-CNN filters were visualised as a temporal plot, and spectral analysis was performed on the extracted weights. The obtained results confirmed that the proposed 1D-CNN was able to generate filters that resemble the EEG wave patterns of different frequencies and spectral analysis confirmed that the filters exploited information from multiple frequency bands (such as alpha band and beta band) that are often associated with a heightened mental state when performing mental tasks.