A synergistic approach for enhanced eye blink detection using wavelet analysis, autoencoding and Crow-Search...

This research endeavor introduces a state-of-the-art, assimilated approach for eye blink detection from Electroencephalography signals. It combines the prominent strategies of wavelet analysis, autoencoding, and a Crow-Search-optimized k-Nearest Neighbors to enhance the performance of eye blink detection from EEG signals. This procedure is initiated by escalating the robustness of EEG data through jittering, which integrates noise into the dataset. Consequently, the wavelet transform is highly demanded during feature extraction in identifying the essential time-frequency components of the signals. These features are further distilled using an autoencoder to provide a dense, yet informative representation. Prior to introducing these features into the machine learning system, they were adjusted. Evidently, the hyperparameters of the k-Nearest Neighbors model have been fine-tuned using Crow Search Algorithm, inspired by the hunting characteristics of crows. This optimization method actively samples the search space to balance exploration and exploitation to identify the optimal configuration for the model. The k-NN model that has been optimized using the proposed method demonstrates significantly higher performance in the eye blink detection process in comparison to the deep learning models when equipped with decorous feature extraction and fine tuning. The effectiveness of the developed system has been ascertained according to the assessment indices such as accuracy, classification reports, and confusion matrix. Thus, the present work offers an optimal method of detecting the eye blink from the EEG signals assisting in the development of further EEG applications including user interfaces, fatigue level identification, and neurological disorders analysis through the enhancement of signal processing and optimization methods. It becomes evident after a detailed evaluation that conventional machine learning algorithms if implemented with correct feature extraction and fine-tuning surpass the deep learning approaches including the frameworks composed of Convolutional Neural Network and Principal Component Analysis and empirical mode decomposition by approximately 96% across all datasets. This proves the advantage of optimized traditional Machine Learning models over the Deep Learning models in realistic EEG-based eye blink detection.