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Basic and Clinical Neuroscience، جلد ۱۴، شماره ۲، صفحات ۰-۰
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in EEG Signal |
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| چکیده انگلیسی مقاله |
The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and left hand MI task. TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely Relief-F, Fisher, Laplacian and local learning based clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and LDA methods are used for classification. Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via Relief-F algorithm as feature selection and SVM classification with 91.02% accuracy. Consequently, TE index and a hierarchical feature selection and classification could be useful for discrimination of right and left hand MI task from multichannel EEG signal. |
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| کلیدواژه‌های انگلیسی مقاله |
Electroencephalogram (EEG), Motor imagery, Effective connectivity, Transfer entropy |
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| نویسندگان مقاله |
| Erfan Rezaei
Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| Ahmad Shalbaf
Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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| نشانی اینترنتی |
http://bcn.iums.ac.ir/browse.php?a_code=A-10-2034-3&slc_lang=en&sid=1 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
Computational Neuroscience |
| نوع مقاله منتشر شده |
Original |
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