References
- M. Wester, “Unspoken Speech Recognition Based On Electroencephalography”, Thesis, Carnegie Mellon University, Pittsburgh, PA, USA,2006.
- M. D’Zmura, S. Deng, T. Lappas, S. Thorpe, and R. Srinivasan, “Toward EEG Sensing of Imagined Speech”, Human-Computer Interaction, Part I, HCII 2009, LNCS 5610, pp:40–48, 2009.
- C. DaSalla, H. Kambara, M. Sato, Y. Koike, “Single-trial classification of vowel speech imagery using common spatial patterns”, Neural Networks 22, pp:1334–1339, 2009. doi: 10.1016/j.neunet.2009.05.008
- K. Brigham, B.V.K. V. Kumar, “Imagined Speech Classification with EEG Signals for Silent Communication: A Preliminary Investigation into Synthetic Telepathy”, 4th International Conference on Bioinformatics and Biomedical Engineering, Chengdu, China, pp: 1–4, 2010.
- X. Chi, J. B. Hagedorn, D. Schoonover and M. D’Zmura, “EEG-Based Discrimination of Imagined Speech Phonemes”, International Journal of Bioelectromagnetism, Vol. 13, No. 4, pp: 201-206, 2011.
- M. Matsumoto, “Silent Speech Decoder using Adaptive Collection”, Proceedings of the companion publication of the 19th international conference on Intelligent User Interfaces, Haifa, Israel, pp-73-76, 2014.
- R. Kamalakkannan, R. Rajkumar, M. Madan Raj., and S. Shenbaga Devi, “Imagined Speech Classification using EEG”, Advances In Biomedical Science And Engineering, Vol. 1, Number 2, pp-20-32, 2014.
- P. Ghane, G. Hossain and A. Tovar, “Robust Understanding of EEG Patterns in Silent Speech”, National Aerospace and Electronics Conference (NAECON), pp: 345–352, 2015.
- C. Cîmpanu, L. Ferariu, F. Ungureanu and T. Dumitriu, “Genetic feature selection for large EEG data with commutation between multiple classifiers”, 21st International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, pp:618-623, 2017.
- S. Bhattacharyya, A. Sengupta, T. Chakraborti, A. Konar and D. N. Tibarewala, “Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata”, Medical & Biological Engineering & Computing, Vol. 52, Issue 2, pp:131–139, 2014. doi: 10.1007/s11517-013-1123-9
- M. Miao, A. Wang and F. Liu, “Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification”, Neural Computing and Applications, Vol. 30, Issue 12, pp:3677–3691, 2018. doi: 10.1007/s00521-017-2950-7
- R. Ghosh, N. Sinha, and S. K. Biswas, “Automated eye blink artefact removal from EEG using support vector machine and autoencoder”, IET Signal Processing, Vol.13, Issue 2, pp:141-148, 4 2019 . doi: 10.1049/iet-spr.2018.5111
- E. Emarya, H. M. Zawbaa and A. E. Hassanien, “Binary grey wolf optimization approaches for feature selection”, Neurocomputing, Vol. 172, 8, pp.:371-381, 2016. doi: 10.1016/j.neucom.2015.06.083
- S. Mirjalili, S. M. Mirjalili and A. Lewis, “Grey Wolf Optimizer”, Advances in Engineering Software, Vol. 69, pp:46–61, 2014. doi: 10.1016/j.advengsoft.2013.12.007
- C. Cortes and V. Vapnik, “Support Vector Networks”, Machine Learning, Vol.20, pp.273–297,1995.
- E. H. Houssein, A. Hamad, A. E. Hassanien and A. A. Fahmy, “Epileptic detection based on whale optimization enhanced support vector machine”, Journal of Information and Optimization Sciences, Vol. 40, No. 3, pp.699-723, 2019. doi: 10.1080/02522667.2018.1453671