Effects of variations in neural network topology and output averaging on the discrimination of mental tasks from spontaneous electroencephalogram

Abstract

Electroencephalogram, or EEG, signals are an important source of information for the study of underlying brain processes. Such studies now provide a framework for the development of a new modality of human-computer interaction based on EEG. Current research in this area only detects a small number of mental states. In this article, EEG from one subject who performed three mental tasks are classified by neural networks. Using a sixth-order autoregressive (AR) model of half-second windows of six-channel EEG, a classification accuracy of 89% on test data is achieved. A cross-validation study of a variety of neural network topologies showed that a network with one hidden layer of 20 units produced the best performance. It was also found that averaging the output of the network over consecutive inputs improved performance. K-means clustering of the resulting neural networks' weights identified key components of the AR representation.