Man-machine communications through brain-wave processing.

The possibility of monitoring voluntarily produced changes in the electroencephalogram (EEG) of a subject and translating these changes into a set of commands to be issued to an external device was investigated. Subjects performed five distinct tasks under both eyes-open and eyes-closed conditions. A feature set consisting of the asymmetry ratios and the power values for each lead at four frequency bands-delta (0-3 Hz) theta (4-7 Hz), alpha (8-13 Hz), and beta (14-20 Hz)-was used to characterize the EEG. The feature sets created from an estimate of the spectral density of the EEG for each task were used to test classification accuracy among the various tasks using a Bayes quadratic classifier. The results show that it is possible to distinguish, to a high degree of accuracy, among the various mental tasks studied, using only the EEG.