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Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification

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Abstract

Motor imagery (MI) tasks evoke event-related desynchronization (ERD) and synchronization (ERS); the ERD-/ERS-related features appearing at specific channels are frequency and time localized. Therefore, optimal channels, frequency band and time interval are of great significance for MI electroencephalography feature extraction. In this paper, channel selection method based on linear discriminant criteria is used to automatically select the channels with high discriminative powers. In addition, the concept of artificial bee colony algorithm is first introduced to find the global optimal combination of frequency band and time interval simultaneously without prior knowledge for common spatial pattern features extraction and classification. Experimental results demonstrate that this scheme can adapt to user-specific patterns and find the relatively optimal channels, frequency band and time interval for feature extraction. The classification results on the BCI Competition III Dataset IVa and BCI Competition IV Dataset IIa clearly present the effectiveness of the proposed method outperforming most of the other competing methods in the literature.

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Acknowledgements

The study is supported by the Jiangsu Province Science and Technology Support Program of China (No. BE2012740). Special thanks to the reviewers for their positive and constructive comments and suggestions on our manuscript.

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Authors and Affiliations

  1. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Minmin Miao, Aimin Wang & Feixiang Liu

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  1. Minmin Miao
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  2. Aimin Wang
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  3. Feixiang Liu
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Correspondence to Aimin Wang.

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Miao, M., Wang, A. & Liu, F. Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification. Neural Comput & Applic 30, 3677–3691 (2018). https://doi.org/10.1007/s00521-017-2950-7

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