Optimized preprocessing and Tiny ML for Attention State Classification

TL;DR

This paper introduces an optimized preprocessing pipeline combined with Tiny ML techniques for classifying attention states from EEG signals, demonstrating high accuracy and efficiency on cognitive load data.

Contribution

It presents a novel approach integrating signal processing and Tiny ML for EEG-based mental state classification, outperforming existing methods.

Findings

Achieves high classification accuracy on EEG data

Outperforms state-of-the-art methods in efficiency

Demonstrates suitability for real-time applications

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.