Deep Neurowave Fusion with Smart Ensemble Stacking for Accurate EEG Eye State Recognition

Signal analysis through electroencephalography (EEG) has become an
imperative method of neural dynamics understanding and the
mechanism of intelligent brain-computer interface applications.
Nonlinearity, noise of the EEG signals, and variability however, pose a
major challenge on identifying the accuracy of classification tasks
especially on eye state detection. The proposed study is an efficient and
computationally optimized machine learning system to classify eye state
using EEG data based on the UCI EEG Eye State dataset. The
suggested methodology consists of systematic data preprocessing,
outliers elimination in the form of interquartile range, features
standardization, and comparative benchmarking of various baseline
classifiers, such as Logistic Regression, Support Vector Machine,
Random Forest and XGBoost. In order to improve the classification
performance, a K-Nearest Neighbor (KNN) model is optimized by
hyperparameter tuning based on cross-validation strategies. The
experimental findings show that the classic linear and kernel-based
models have low performance because of the complicated nature of
EEG signals with accuracy of about 63 percent and 55 percent,
respectively. Random Forest and XGBoost ensemble-based models
result in a higher level of performance with accuracy of up to 93.
Nevertheless, the proposed optimization KNN model results in the
highest level of classification accuracy with 98 percent and equal
precision, recall and F1- score values. Additional statistical
confirmation on the basis of one-way ANOVA proves that the
performance change is significant (p < 0.05). The remaining analysis of
residuals, QQ, and homoscedasticity test confirm the stability and
resilience of the suggested model. The results show that distance based
learning when optimized well can greatly be used in capturing fine
differences in EEG signals. The suggested structure provides an
effective, interpretive and computationally efficient response to EEG
based eye state recognition and has a high potential of real-time
cognitive monitoring, driver drowsiness recognition and brain
computer interface systems.