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 and compared it to other state-of-the-art methods. 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.
@article{arxiv.2303.11371,
title = {Optimized preprocessing and Tiny ML for Attention State Classification},
author = {Yinghao Wang and Rémi Nahon and Enzo Tartaglione and Pavlo Mozharovskyi and Van-Tam Nguyen},
journal= {arXiv preprint arXiv:2303.11371},
year = {2023}
}