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

fYear

2005

fDate

16-19 March 2005

Firstpage

574

Lastpage

576

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on

Conference_Location

Arlington, VA

Print_ISBN

0-7803-8710-4

Type

conf

DOI

10.1109/CNE.2005.1419688

Filename

1419688

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3012629