Classification of neural signals from sparse autoregressive features

This paper introduces a signal classification framework that can be used for brain–computer interface design. The actual classification is performed on sparse autoregressive features. It can use any wellknown classification algorithm, such as discriminant analysis, linear logistic regression and support vector machines. The autoregressive coefficients of all signals and channels are simultaneously to the variable selection capability of the group lasso, the framework can drop individual autoregressive coefficients that are useless in the prediction stage. Also, the framework is relatively insensitive to the chosen autoregressive order. We devise an efficient algorithm to solve this problem. We test our approach on Keirn and Aunon’s data, used for binary classification of electroencephalogram signals, achieving promising results. Crown Copyright & 2013 Published by Elsevier B.V. All rights reserved.