The classification of wink-based eeg signals: An evaluation of different transfer learning models for feature extraction - Jothi Letchumy, Mahendra Kumar; Mamunur, Rashid; Rabiu Muazu, Musa; Mohd Azraai, Mohd Razman; Norizam, Sulaiman; Rozita, Jailani; Anwar, P. P. Abdul Majeed (Springer, 2021)
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.