Motor imagery classification for brain-computer interfaces through a chaotic neural network

In this paper, we propose to enhance the detection of control states in online brain-computer interfaces (BCI) with the use of the biologically inspired K-set neural network. This neural network was initially built to model brain waves of small sets of neurons in the brain and later showed a great capability of encoding complex and noisy data into oscillation patterns. We apply the K-set network to classification of motor imagery, a type of mental state very useful for BCI applications. Experimental results show that the network can work efficiently in this task and thus provide better control for BCI applications.