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arXiv · 2303.11371

Optimized preprocessing and Tiny ML for Attention State Classification

Yinghao Wang·Rémi Nahon·Enzo Tartaglione·Pavlo Mozharovskyi·Van-Tam Nguyen
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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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Yinghao Wang, Rémi Nahon, Enzo Tartaglione, Pavlo Mozharovskyi, Van-Tam Nguyen. 2023-03-20. Optimized preprocessing and Tiny ML for Attention State Classification. https://doi.org/10.1109/ssp53291.2023.10207930

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