Optimized preprocessing and Tiny ML for Attention State Classification

Yinghao Wang, Rémi Nahon, Enzo Tartaglione, Pavlo Mozharovskyi, Van-Tam Nguyen
3/20/2023
cs.LGeess.SP

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Abstract

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.

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Cite this paper

@article{wang2023optimized,
  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},
  year   = {2023},
  eprint = {2303.11371},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.11371v1}
}

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