NDLI: GMM-Based Classification Method for Continuous Prediction in Brain-Computer Interface

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyuan Zhu Jiankang Wu Yimin Cheng Yixiao Wang
Copyright Year	2006
Description	Author affiliation: Dept. of Electron. Sci. & Technol., USTC, Hefei (Xiaoyuan Zhu)
Abstract	Brain-computer interface (BCI) requires effective classification algorithms for electroencephalogram (EEG) signal processing. To train a classifier for continuous prediction, trials in training dataset are first divided into segments. The difficulty here is how to combine the predictions across time to make the final decision of a whole trial as early and as accurately as possible. In this paper, we propose a novel statistical approach based on Gaussian mixture models (GMM) to classify the EEG trials by combining the predictions of segments according to the discriminative powers at individual time intervals during a trial. We evaluate the proposed method on two datasets of BCI competition 2003 and 2005. The experimental results have shown that the performance of the proposed method is among the best
Sponsorship	IEEE CPS
Starting Page	1171
Ending Page	1174
File Size	128270
Page Count	4
File Format	PDF
ISBN	0769525210
ISSN	10514651
DOI	10.1109/ICPR.2006.610
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain computer interfaces Electroencephalography Predictive models Signal processing algorithms Brain modeling Power system modeling Classification algorithms Communication channels Fatigue Bayesian methods
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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