Robust classification of EEG signal for brain-computer interface
We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller.
P300, brain-computer interface, event related potential, speller, support vector machine (SVM)
Computer Sciences | Graphics and Human Computer Interfaces
Data Management and Analytics
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
M. THULASIDAS; GUAN, Cuntai; and WU, Jiankang.
Robust classification of EEG signal for brain-computer interface. (2006). IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING. 14, (1), 24-29. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3491
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