Optimized preprocessing and Tiny ML for Attention State Classification

Electroencephalography has been widely used to study mental processes such as attention, perception, and emotion. This is because mental state classification has important applications in many fields, including healthcare, human-computer interaction, and education.In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.