Application of competitive hopfield neural network to brain-computer interface systems

Wei Yen Hsu

研究成果: 雜誌貢獻文章同行評審

49 引文 斯高帕斯(Scopus)

摘要

We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.
原文英語
頁(從 - 到)51-62
頁數12
期刊International Journal of Neural Systems
22
發行號1
DOIs
出版狀態已發佈 - 2月 2012
對外發佈

ASJC Scopus subject areas

  • 電腦網路與通信

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