Deep learning-based classification for brain-computer interfaces

Thomas, J; Maszczyk, T; Sinha, N; Kluge, T; Dauwels, J

doi:10.1109/SMC.2017.8122608

Deep learning-based classification for brain-computer interfaces

Lookup NU author(s): Nishant Sinha

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Abstract

© 2017 IEEE. Brain-computer interface (BCI) is an emerging area of research that aims to improve the quality of human-computer applications. It has enormous scope in biomedical applications, neural rehabilitation, biometric authentication, educational pro-grammes, and entertainment applications. A BCI system has four major components: signal acquisition, signal preprocessing, feature extraction, and classification. In this study, we provide a comparison of various traditional classification algorithms to the newer methods of deep learning. We explore two different types of deep learning methods, namely, convolutional neural networks (CNN) and recurrent neural networks (RNN) with long short-term memory (LSTM) architecture. We test the classification accuracies on a recent 5-class steady-state visual evoked potential (SSVEP) dataset. The results prove the superiority of deep learning methods in comparison with the traditional classification algorithms. Amongst the traditional classifiers, support vector machine (SVM) with Gaussian kernel employing sequential forward selection (SFS) of features provided a better classification accuracy of 66.09%, while CNN provided the highest classification accuracy of 69.03%.