Title :
Enhancing Classification by Perceptual Characteristic for the P300 Speller Paradigm
Author :
Lin, Z.L. ; Zhang, C.S.
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing
Abstract :
We find that in P300 speller paradigm, there exists difference between electroencephalogram (EEG) signals evoked by flashing adjacent to the row (column) containing the target and those evoked by other non-target stimuli. This difference might arise out of users´ perceptual confusion. We use a machine learning approach to learn the difference of this perceptual characteristic, and employ the classifier to aid classification in P300 speller. Experiments show that our method remarkably improves the performance
Keywords :
electroencephalography; learning (artificial intelligence); medical signal processing; signal classification; P300 speller paradigm; electroencephalogram signals; machine learning; perceptual characteristics; signal classification; Automation; Blindness; Displays; Electroencephalography; Machine learning; Object detection; Pattern recognition; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-8710-4
DOI :
10.1109/CNE.2005.1419688
