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In this paper, we explore prior research and introduce a new methodology forclassifying mental state levels based on EEG signals utilizing machine learning(ML). Our method proposes an optimized training method by introducing avalidation set and a refined standardization process to rectify data leakageshortcomings observed in preceding studies. Furthermore, we establish novelbenchmark figures for various models, including random forest and deep neuralnetworks.

Enhanced EEG-Based Mental State Classification : A novel approach to eliminate data leakage and improve training optimization for Machine Learning