Abstract
This work reports the potentiality of the motor imagery movement classification from prefrontal hemodynamics for the brain–computer interface (BCI) applications. Although movement-related activation correlates with the central lobe, this area of a paralyzed patient is often found obsolete. Therefore, to design a BCI system for paralyzed persons, the central lobe hemodynamics cannot be considered. To overcome this problem, this work proposed an alternative approach. This research work experimentally investigates the potentiality of classifying the motor planning (imagery) activities from the prefrontal hemodynamics. The functional changes of prefrontal hemodynamics for imagery hand movements are measured by functional near-infrared spectroscopy (fNIRS) from several subjects. The fNIRS signals of imagery hand movements are classified by a k-nearest neighbor and artificial neural network algorithms. The classification accuracies were checked with the subject dependent and independent approach. Our results demonstrate that the prefrontal hemodynamics could serve as the potential biomarker for the effective BCI system.
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Acknowledgements
In this paper, the fNIRS data were taken from five subjects: (i) Md. Al Noman, (ii) Md. Torikul Islam Piash, (iii) Md. Ahnaf Tahmid Ifty, (iv) Md. Sohidujjaman Sojib, and (v) Md. Abu Bakar Siddik Yusha. The authors would like to thank them for providing their kind consent to use their data in this research work. Moreover, special thanks to Md. Al Noman for giving his consent to use his picture in Fig. 2b. The data acquisition protocol and the consents of the participants were ethically approved by Data Acquiring Ethics Evaluation Committee (DAEEC) of Khulna University of Engineering & Technology (KUET), Bangladesh.
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Asadur Rahman, M., Mahmudul Haque, M., Anjum, A., Nurunnabi Mollah, M., Ahmad, M. (2020). Classification of Motor Imagery Events from Prefrontal Hemodynamics for BCI Application. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_2
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DOI: https://doi.org/10.1007/978-981-13-7564-4_2
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