Optimized preprocessing and Tiny ML for Attention State Classification

• URL: http://arxiv.org/abs/2303.11371v1
• Date: Mon, 20 Mar 2023 18:17:35 GMT
• Title: Optimized preprocessing and Tiny ML for Attention State Classification
• Authors: Yinghao Wang, R\'emi Nahon, Enzo Tartaglione, Pavlo Mozharovskyi, and Van-Tam Nguyen
• Abstract summary: We present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task.
• Score: 2.7810511835091427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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