Optimized preprocessing and Tiny ML for Attention State Classification

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.

References in corpus (1)

• FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data

Cited by in corpus (1)

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