From Auto ML to Stacking Ensembles Advancing EEG Cognitive State Prediction Techniques

This paper explores the efficacy of an Auto ML-based stacked ensemble model in EEG-based cognitive state prediction, compared against traditional machine learning models. Utilizing a comprehensive evaluation involving metrics such as MAE, MSE, RMSE, MAPE, and R2 Score, the study highlights the superior performance of the proposed model. Achieving notably lower error rates (MAE = 0.08, MSE = 0.10, RMSE = 0.32, MAPE = 0.85) and the highest R2 Score of 0.96, the proposed model demonstrates a significant advancement over traditional approaches. This work contributes to the ongoing development of predictive models in neuroscience, showcasing the potential of Auto ML to enhance model accuracy and efficiency in interpreting complex EEG data