Abstract
The findings of two recent studies that aim at developing a self-paced brain interface (BI) system with low false positive rates are discussed. The first study examines the use of information extracted from different neurological phenomena and the second study examines the wavelet coefficients extracted from a single neurological phenomenon. The analysis of the data of two subjects shows that both are successful at yielding low false positive rates. These studies also show that for each subject, a unique set of features and EEG channels lead to superior performance.
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