Performance Evaluation of Current State of Brain Computer Interface (BCI) and its Trends
DOI:
https://doi.org/10.24113/ijoscience.v8i1.456Keywords:
BCI, BMI, EEG, EOG, EMG, Stacked deep auto encoder,Abstract
The goal of this study was to broaden a technique to the rapidly growing field of BCI analysis. The goal was to broaden a deep understanding of the neurophysiological processes that might be overloaded to incorporate a BCI system by focusing in the electroencephalogram as the BCI input modality. The research is being carried out in order to gain a better understanding of the human brain's neurophysiology. The purpose of this research is to look into electroencephalography as a method of detecting mental activity. We present a comprehensive overview of EEG-based BCI systems that have been implemented so far. We also explored the future of BCI technology and evaluated by comparing the performance of various feature classification techniques
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Nourmohammadi, A., Jafari, M., & Zander, T. O. (2018). A survey on unmanned aerial vehicle remote control using brain-computer interface. IEEE Transactions on Human-Machine Systems, 48(4), 337–348. https://doi.org/10.1109/THMS.2018.2830647 DOI: https://doi.org/10.1109/THMS.2018.2830647
Coogan, C. G., & He, B. (2018). Brain-Computer Interface Control in a Virtual Reality Environment and Applications for the Internet of Things. IEEE Access, 6(c), 10840–10849. https://doi.org/10.1109/ACCESS.2018.2809453 DOI: https://doi.org/10.1109/ACCESS.2018.2809453
Aggarwal, S., & Chugh, N. (2019). Signal processing techniques for motor imagery brain computer interface: A review. Array, 1–2(June), 100003. https://doi.org/10.1016/j.array.2019.100003 DOI: https://doi.org/10.1016/j.array.2019.100003
Jin, Z., Zhou, G., Gao, D., & Zhang, Y. (2020). EEG classification using sparse Bayesian extreme learning machine for brain–computer interface. Neural Computing and Applications, 32(11), 6601–6609. https://doi.org/10.1007/s00521-018-3735-3 DOI: https://doi.org/10.1007/s00521-018-3735-3
Bicík, V., Golda, V., & Lison?k, P. (1970). Optico-motor coordination in intact and callostomized cats. Activitas Nervosa Superior, 12(2), 169–170.
C. M. Bishop et al., Pattern recognition and machine learning. Springer, 2006, vol. 1.
R. Rojas, “The secret life of the covariance matrix, ” Freie University Berlin, [online]Available:http://www.infuberlin.de/agki/rojas_home/documents/secretcovariance, 2009.
Y. K. Cheung & J. H. Klotz,“The mann-whitney wilcoxon distribution using linked list” Statistica Sinica, 1997, vol. 7, issue. 3, pp. 805–813.
P. Pittner, “An algorithm for the mann-whitney utest,” Biometrical Journal,1981, vol.23 issue. 1, pp.105–107. DOI: https://doi.org/10.1002/bimj.4710230113
Ang, Sakel M, Pepper M, Phillips M. “Use of brain computer interfaces in neurological rehabilitation” Brit J Neurosci Nurs 2011, vol. 7, issue 3, pp.523–528. DOI: https://doi.org/10.12968/bjnn.2011.7.3.523
Vecchiato G, Astolfi L, De Vico Fallani, Salinari S, Cincotti F, Aloise F, Mattia D, MG Marciani, Bianchi L,”The study of brain activity during the observation of commercial advertising by using high resolution EEG techniques”, Engineering in Medicine and Biology Society, IEEE, 2009. pp. 57–60. DOI: https://doi.org/10.1109/IEMBS.2009.5335045
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Copyright (c) 2022 Aparajita Rajoria, Prof. Shivank Soni
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