GRAZ BRAIN-COMPUTER INTERFACE-II - TOWARDS COMMUNICATION BETWEEN HUMANS AND COMPUTERS BASED ON ONLINE CLASSIFICATION OF 3 DIFFERENT EEG PATTERNS

The paper describes work on the brain-computer interface (BCI). The BC I is designed to help patients with severe motor impairment (e.g. amyo tropic lateral sclerosis) to communicate with their environment throug h wilful modification of their EEG. To establish such a communication channel, two major prerequisites have to be fulfilled: features that r eliably describe several distinctive brain states have to be available , and these features must be classified on-line, i.e. on a single-tria l basis. The prototype Graz BCI II, which is based on the distinction of three different types of EEG pattern, is described, and results of online and offline classification performance of four subjects are rep orted. The online results suggest that, in the best case, a classifica tion accuracy of about 60% is reached after only three training sessio ns. The offline results show how selection of specific frequency bands influences the classification performance in single-trial data.