High accuracy classification of EEG signal

Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This work presents a high accuracy EEG signal classification method using single trial EEC signal to detect left and right finger movement. We apply an op...

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Bibliographic Details
Main Authors: Wenjie Xu, Cuntain Guan, Chng Eng Siong, Ranganatha, S., Thulasidas, M., Jiankand Wu
Format: Conference Proceeding
Language:English
Subjects:
Application software
Brain computer interfaces
Electroencephalography
Feature extraction
Filters
Fingers
Laboratories
Pattern classification
Signal detection
Signal generators
Online Access:Request full text
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Summary:Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This work presents a high accuracy EEG signal classification method using single trial EEC signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result shows that the classification accuracy of the proposed method reaches 90% as compared to the current reported best accuracy of 84%.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2004.1334229