EEG Features Correlated with Performance in P300-Based BCI Operation: a Long-Term Case Study in a Home User with Amyotrophic Lateral Sclerosis (ALS)

Brain-computer interface (BCI) technology holds promise to restore the communication and control ability of individuals with severe motor disabilities (Wolpaw et al. 2002). An EEG-based BCI system that detects the P300 event-related potential (ERP) allows users to select items from a matrix consisting of letters, numbers, and function calls (after the method of Donchin et al., 2000) using brain signals rather than the brain’s normal output pathways of peripheral nerves and muscles. Our laboratory seeks to realize independent home use of P300-based BCI by severely disabled individuals. In an earlier study, we found that P300-based BCI performance (i.e., accurate classification) on test data was correlated with the test data and was not correlated with the training data (Mak et al. 2009). The present study set out