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A fuzzy-based classification strategy (FBCS) based on brain–computer interface

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Abstract

Brain–computer interface systems help paralyzed people to control devices such as a computer cursor, robotic limbs, wheelchairs, or spellers by only using their thoughts. Nowadays, electroencephalogram (EEG) signals are mostly used to detect activity of various actions within the brain as they provide rich information about brain’s electrical activity. However, EEG signal generates large amount of data which is usually difficult to interpret and classify. This paper introduces a new classification strategy based on EEG signals, which is called fuzzy-based classification strategy (FBCS). FBCS minimizes the classification time by perfectly extracting the effective features of the produced EEG signals based on a set of elected electrodes using semantic analysis, then taking the classification decision accordingly. FBCS uses feature reduction and electrode selection techniques to reduce the dimensionality of data to be classified, which also improves the classification accuracy. Experimental results have shown that FBCS outperforms recent classification strategies in terms of accuracy and classification time.

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Authors and Affiliations

  1. Department of Computer Engineering and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Ahmed I. Saleh, Sahar A. Shehata & Labeeb M. Labeeb

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  1. Ahmed I. Saleh
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  2. Sahar A. Shehata
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  3. Labeeb M. Labeeb
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Correspondence to Ahmed I. Saleh.

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We admit that our paper is well formed due to the ethical standards of the declared author policy. We also declare that they have no conflict of interest. Moreover, informed consent was obtained from all individual participants included in the study.

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Communicated by V. Loia.

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Saleh, A.I., Shehata, S.A. & Labeeb, L.M. A fuzzy-based classification strategy (FBCS) based on brain–computer interface. Soft Comput 23, 2343–2367 (2019). https://doi.org/10.1007/s00500-017-2930-y

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