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Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification.
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- Author(s): Miao, Minmin; Wang, Aimin; Liu, Feixiang
- Source:
Neural Computing & Applications. Dec2018, Vol. 30 Issue 12, p3677-3691. 15p. - Source:
- Additional Information
- Subject Terms:
- 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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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