Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/54335
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Type: Conference paper
Title: Comparative analysis of signal processing in brain computer interface
Author: Yang, R.
Gray, D.
Ng, B.
He, M.
Citation: Proceedings of the 4th IEEE Conference on ICIEA 2009: pp.580-585
Publisher: IEEE
Publisher Place: CD
Issue Date: 2009
ISBN: 9781424428007
Conference Name: IEEE Conference on Industrial Electronics and Applications (4th : 2009 : Xi'an, China)
Statement of
Responsibility: 
Ruiting Yang, D.A Gray, B.W Ng and Mingyi He
Abstract: Brain computer interface (BCI) systems utilise Electroencephalography (EEG) to translate specific human thinking activities into control commands. An essential part of any BCI is a pattern recognition system. In this paper, a number of different features and classifiers are compared in terms of classification accuracy and computation time. Two typical features are studied: autoregressive (AR) and spectrum components along with three different classifiers; the K-nearest neighbor, linear discriminant analysis (LDA) and Bayesian statistical classifiers. The results showed that all classifiers achieved very high accuracies and short computation times.
Keywords: Electroencephalography (EEG); brain computer; interface; classifier; feature
RMID: 0020090897
DOI: 10.1109/ICIEA.2009.5138215
Appears in Collections:Electrical and Electronic Engineering publications

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