Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data

Wei Yen Hsu, Chao Hung Lin, Hsien Jen Hsu, Po Hsun Chen, I. Ru Chen

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54 引文 斯高帕斯(Scopus)

摘要

In this study, we propose an analysis system for single-trial classification of electroencephalogram (EEG) data. Combined with automatic EOG artifact removal and wavelet-based amplitude modulation (AM) features, the support vector machine (SVM) classifier is applied to the classification of left finger lifting and resting. Automatic EOG artifact removal is proposed to eliminate the EOG artifacts automatically by means of independent component analysis (ICA) and correlation coefficient. The features are then extracted from the discrete wavelet transform (DWT) data by the AM method. Finally, the SVM is used for the discriminant of wavelet-based AM features. Compared with EEG data without EOG artifact removal, band power features and LDA classifier, the proposed system achieves promising results in classification accuracy.
原文英語
頁(從 - 到)2743-2749
頁數7
期刊Expert Systems with Applications
39
發行號3
DOIs
出版狀態已發佈 - 2月 15 2012
對外發佈

ASJC Scopus subject areas

  • 人工智慧
  • 電腦科學應用
  • 工程 (全部)

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