# Optimized preprocessing and Tiny ML for Attention State Classification

arXiv: https://arxiv.org/abs/2303.11371 · HTML: https://mlsanity.com/paper/2303.11371/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [Learning Robust Features using Deep Learning for Automatic Seizure Detection](https://mlsanity.com/paper/1608.00220/) (2016)
- [FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data](https://mlsanity.com/paper/2206.02792/) (2022)

## Built on by (most cited first, 1 of 1)

- [SCoTTi: Save Computation at Training Time with an adaptive framework](https://mlsanity.com/paper/2312.12483/) (2023)

## Abstract (arXiv)

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.