Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling

The Mind Speller is a Brain-Computer Interface which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms. This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. We report on the implementation of a feature extraction procedure on a new ultra low-power 8-channel wireless EEG device. The feature extraction procedure is based on downsampled EEG signal epochs, the Student's t-statistic of the Continuous Wavelet Transform, and the Common Spatial Pattern technique. For classification, we use a linear Least-Squares Support Vector Machine. The results show that subjects are potentially able to communicate a character in less than ten seconds with an accuracy of 94.5%, which is more than twice as fast as the state of the art. In addition since our EEG device is wireless it offers an increased comfort to the subject.

• Type: Journal
• Year: 2009
• Where: ICMLA
• Authors: Adrien Combaz, Nikolay V. Manyakov, Nikolay Chumerin, Johan A. K. Suykens, Marc M. Van Hulle
• Keywords: EEG Device, Feature Extraction Procedure, ICMLA 2009, Machine Learning, Wireless Eeg Device