Abstract
Electroencephalogram (EEG) is non-trivial in the diagnosis and treatment of neurogenerative diseases. Brain-Computer Interface (BCI) that utilises EEG is often used to improve the activities of daily living of patients with the aforesaid disorder. In this study, the efficacy of different Transfer Learning (TL) models, i.e., ResNet50, ResNet101 and ResNet152 in extracting features to classify wink-based EEG signals is evaluated. The time–frequency spectrum transformation of the Right-Wink, Left-Wink, and No-Wink based on EEG signals was achieved via Discrete Wavelet Transform (DWT). The extracted features were then fed into different variation of Support Vector Machine (SVM) classifiers to evaluate the performance of the different feature extraction method in classifying the wink class. The data are divided into training, validation, ad test, with a stratified ratio of 60:20:20. It was shown from the study, that the features extracted via ResNet152 were better than that of ResNet50 and ResNet101. The overall validation and test accuracy attained through the ResNet152 model is approximately 92%. Henceforth, it could be concluded that the proposed pipeline suitable to be adopted to classify wink-based EEG signals for different BCI applications.
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References
Ganasegeran K, Fadzly M, Jamil A, Sivasampu S (2019) Discover! Malaysia’s stroke care revolution—special edition. ResearchGate 2:1–32
Ab Patar MNA, Said AF, Mahmud J, Majeed APPA, Razman MA (2014) System integration and control of dynamic ankle foot orthosis for lower limb rehabilitation. In: ISTMET 2014—1st international symposium technology management emerging technology Proceedings, vol 2, pp 82–85. https://doi.org/10.1109/ISTMET.2014.6936482
Shih JJ, Krusienski DJ, Wolpaw JR (2012) Brain-computer interfaces in medicine. Mayo Clin Proc 87:268–279. https://doi.org/10.1016/j.mayocp.2011.12.008
Vaughan TM (2003) Brain-computer interface technology: a review of the second international meeting. IEEE Trans Neural Syst Rehabil Eng 11:94–109. https://doi.org/10.1109/TNSRE.2003.814799
Lin JS, Hsieh CH (2016) A wireless BCI-controlled integration system in smart living space for patients. Wirel Pers Commun 88:395–412. https://doi.org/10.1007/s11277-015-3129-0
Rashid M, Sulaiman N, Majeed APPA, Musa RM, Ahmad AF, Bari BS, Khatun S (2020) Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review. Front Neurorobot 14:1–35. https://doi.org/10.3389/fnbot.2020.00025
Domrös F, Störkle D, Ilmberger J, Kuhlenkötter B (2013) Converging clinical and engineering research on neurorehabilitation. Converg Clin Eng Res Neurorehab 1:409–413. https://doi.org/10.1007/978-3-642-34546-3
Huang Y, Yang J, Liu S, Pan J (2019) Combining facial expressions and electroencephalography to enhance emotion recognition. Futur Internet 11:1–17. https://doi.org/10.3390/fi11050105
Choy TTC, Leung PM (1988) Real time microprocessor-based 50 Hz notch filter for ECG. J Biomed Eng 10:285–288. https://doi.org/10.1016/0141-5425(88)90013-1
Jayant HK, Rana KPS, Kumar V, Nair SS, Mishra P (2006) Efficient IIR notch filter design using minimax optimisation for 50 Hz noise suppression in ECG. In: Proceedings of 2015 international conference on signal processing computing control. ISPCC 2015, pp 290–295. https://doi.org/10.1109/ISPCC.2015.7375043
Leske S, Dalal SS (2019) Reducing power line noise in EEG and MEG data via spectrum interpolation. Neuroimage 189:763–776. https://doi.org/10.1016/j.neuroimage.2019.01.026
Bekbalanova M, Zhunis A, Duisebekov Z (2019) Epileptic seizure prediction in EEG signals using EMD and DWT. In: 2019 15th international conference on electronics comput. Comput. 1–4 (2019)
Gholami R, Fakhari N (2017) Support vector machine: principles, parameters, and applications. Elsevier Inc. https://doi.org/10.1016/B978-0-12-811318-9.00027-2
Yang J, Singh H, Hines EL, Schlaghecken F, Iliescu DD, Leeson MS, Stocks NG (2012) Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif Intell Med 55:117–126. https://doi.org/10.1016/j.artmed.2012.02.001
World Health Organization (2008) Neurological disorders. Public Health Challenges. J Nerv Ment Dis 196:176. https://doi.org/10.1097/nmd.0b013e31816372ab
Musa RM, Majeed APPA, Taha Z, Chang SW, Nasir AF, Abdullah MR (2019) A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLoS One 14:1–12. https://doi.org/10.1371/journal.pone.0209638
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The present study is funded by Universiti Malaysia Pahang via RDU180321.
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Mahendra Kumar, J.L. et al. (2022). The Classification of Wink-Based EEG Signals: An Evaluation of Different Transfer Learning Models for Feature Extraction. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_6
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