Fractal analysis of motor imagery recognition in the BCI research

Chia-Tzu Chang; Han-Pang Huang; Tzu-Hao Huang

doi:10.1117/12.905078

15 November 2011 Fractal analysis of motor imagery recognition in the BCI research

Chia-Tzu Chang, Han-Pang Huang, Tzu-Hao Huang

Proceedings Volume 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation; 83212L (2011) https://doi.org/10.1117/12.905078
Event: Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 2011, Yunnan, China

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).

Citation Download Citation

Chia-Tzu Chang, Han-Pang Huang, and Tzu-Hao Huang "Fractal analysis of motor imagery recognition in the BCI research", Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 83212L (15 November 2011); https://doi.org/10.1117/12.905078

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