Neural network pruning for feature selection - Application to a P300 Brain-Computer Interface

A Brain-Computer Interface (BCI) is an interface that enables the direct communication between human and machines by analyzing brain measurements. A P300 speller is based on the oddball paradigm, which generates event-related potential (ERP), like the P300 wave, on targets selected by the user. The detection of these P300 waves allows selecting visually characters on the screen. We present a new model for the detection of P300 waves. The techniques is based on a neural network that uses convolution layers for creating channels. One challenge for improving pragmatically BCIs is to reduce the number of electrodes and to select the best electrodes in relation to the subject particularities. We propose a feature selection strategy based on salient connexions in the first hidden layer of a neural network trained with all the electrodes as input. A new classifier is created in relation to the remaining topology and the desired number of electrodes for the system. The recognition rate of the P300 speller over two subjects is 87% by considering only 8 electrodes.