Using time-dependent neural networks for EEG classification.

Abstract

This paper compares two different topologies of neural networks. They are used to classify single trial electroencephalograph (EEG) data from a brain-computer interface (BCI). A short introduction to time series classification is given, and the used classifiers are described. Standard multilayer perceptrons (MLPs) are used as a standard method for classification. They are compared to finite impulse response (FIR) MLPs, which use FIR filters instead of static weights to allow temporal processing inside the classifier. A theoretical comparison of the two architectures is presented. The results of a BCI experiment with three different subjects are given and discussed. These results demonstrate the higher performance of the FIR MLP compared with the standard MLP.