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Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Shereen A. El-aal, Rabie A. Ramadan, Neveen I. Ghali
Copyright: © 2016 |Volume: 5 |Issue: 4 |Pages: 19
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781466693616|DOI: 10.4018/IJSDA.2016100104
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MLA

Shereen A. El-aal, et al. "Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System." IJSDA vol.5, no.4 2016: pp.64-82. http://doi.org/10.4018/IJSDA.2016100104

APA

Shereen A. El-aal, Ramadan, R. A., & Ghali, N. I. (2016). Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System. International Journal of System Dynamics Applications (IJSDA), 5(4), 64-82. http://doi.org/10.4018/IJSDA.2016100104

Chicago

Shereen A. El-aal, Rabie A. Ramadan, and Neveen I. Ghali. "Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System," International Journal of System Dynamics Applications (IJSDA) 5, no.4: 64-82. http://doi.org/10.4018/IJSDA.2016100104

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Abstract

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

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