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Differential Evolution Algorithm as a Tool for Optimal Feature Subset Selection in Motor Imagery EEG

Muhammad Zeeshan Baig, Nauman Aslam, Hubert P. H. Shum and Li Zhang
Expert Systems with Applications (ESWA), 2017

 Impact Factor: 8.5 Top 25% Journal in Computer Science, Artificial Intelligence Citation: 150#

Differential Evolution Algorithm as a Tool for Optimal Feature Subset Selection in Motor Imagery EEG
# According to Google Scholar 2023"

Abstract

One of the challenges in developing a Brain Computer Interface (BCI) is dealing with the high dimensionality of the data when extracting features from EEG signals. Different feature selection algorithms have been proposed to overcome this problem but most of them involve complex transformed features, which require high computation and also result in increasing size of the feature set. In this paper, we present a new hybrid method to select features that involves a Differential Evolution (DE) optimization algorithm for searching the feature space to generate the optimal feature subset, with performance evaluated by a classifier. We provide a comprehensive study of the significance of evolutionary algorithm in selecting the best features for EEG signals. The BCI competition III, dataset IVa has been used to evaluate the method. Experimental results demonstrate that the proposed method performs well with Support Vector Machine (SVM) classifier, with an average classification accuracy of above 95% with a minimum of just 10 features. We also present a comparison of Differential Evolution (DE) with other evolutionary algorithms, and the results show the superiority of DE which implies that, with the selection of a good searching algorithm, a simple Common Spatial Pattern filter features can produce good results.

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BibTeX

@article{baig17differential,
 author={Baig, Muhammad Zeeshan and Aslam, Nauman and Shum, Hubert P. H. and Zhang, Li},
 journal={Expert Systems with Applications},
 title={Differential Evolution Algorithm as a Tool for Optimal Feature Subset Selection in Motor Imagery EEG},
 year={2017},
 volume={90},
 pages={184--195},
 numpages={12},
 doi={10.1016/j.eswa.2017.07.033},
 issn={0957-4174},
 publisher={Elsevier},
}

RIS

TY  - JOUR
AU  - Baig, Muhammad Zeeshan
AU  - Aslam, Nauman
AU  - Shum, Hubert P. H.
AU  - Zhang, Li
T2  - Expert Systems with Applications
TI  - Differential Evolution Algorithm as a Tool for Optimal Feature Subset Selection in Motor Imagery EEG
PY  - 2017
VL  - 90
SP  - 184
EP  - 195
DO  - 10.1016/j.eswa.2017.07.033
SN  - 0957-4174
PB  - Elsevier
ER  - 

Plain Text

Muhammad Zeeshan Baig, Nauman Aslam, Hubert P. H. Shum and Li Zhang, "Differential Evolution Algorithm as a Tool for Optimal Feature Subset Selection in Motor Imagery EEG," Expert Systems with Applications, vol. 90, pp. 184-195, Elsevier, 2017.

Supporting Grants

Similar Research

Muhammad Zeeshan Baig, Nauman Aslam and Hubert P. H. Shum, "Filtering Techniques for Channel Selection in Motor Imagery EEG Applications: A Survey", Artificial Intelligence Review (AIRE), 2020
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Worasak Rueangsirarak, Jingtian Zhang, Nauman Aslam and Hubert P. H. Shum, "Automatic Musculoskeletal and Neurological Disorder Diagnosis with Relative Joint Displacement from Human Gait", IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), 2018

 

 

Last updated on 24 February 2024
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