Adaptive brain interfaces - ABI: simple features, simple neural network, complex brain-actuated devices

Abstract

This paper reports results with ABI, a portable noninvasive brain-computer interface. It uses 8 scalp electrodes to measure spontaneous EEG signals from which we extract simple power spectral features. The features are fed to a simple local neural network that recognizes reliably 3 different mental states. We compare the performance of this local classifier to more complex time-processing neural networks. We also illustrate the control of a complex brain-actuated device; i.e., a robot moving along smooth and safe paths between rooms.