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Proceedings Paper

Fractal analysis of motor imagery recognition in the BCI research
Author(s): Chia-Tzu Chang; Han-Pang Huang; Tzu-Hao Huang
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Paper Abstract

A fractal approach is employed for the brain motor imagery recognition and applied to brain computer interface (BCI). The fractal dimension is used as feature extraction and SVM (Support Vector Machine) as feature classifier for on-line BCI applications. The modified Inverse Random Midpoint Displacement (mIRMD) is adopted to calculate the fractal dimensions of EEG signals. The fractal dimensions can effectively reflect the complexity of EEG signals, and are related to the motor imagery tasks. Further, the SVM is employed as the classifier to combine with fractal dimension for motor-imagery recognition and use mutual information to show the difference between two classes. The results are compared with those in the BCI 2003 competition and it shows that our method has better classification accuracy and mutual information (MI).

Paper Details

Date Published: 15 November 2011
PDF: 10 pages
Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 83212L (15 November 2011); doi: 10.1117/12.905078
Show Author Affiliations
Chia-Tzu Chang, National Taiwan Univ. (Taiwan)
Han-Pang Huang, National Taiwan Univ. (Taiwan)
Tzu-Hao Huang, National Taiwan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 8321:
Seventh International Symposium on Precision Engineering Measurements and Instrumentation
Kuang-Chao Fan; Man Song; Rong-Sheng Lu, Editor(s)

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