STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs

TL;DR

This paper introduces STEAM-EEG, a novel framework that transforms EEG signals into visual images using Markov Transfer Fields and employs advanced graphics techniques for improved analysis and pattern recognition.

Contribution

It presents a new method combining Markov Transfer Fields with computer graphics to enhance EEG signal visualization and interpretation.

Findings

Effective spatiotemporal EEG analysis using MTF-based imaging

Improved pattern recognition through visual transformation of EEG data

Facilitates better data exploration and decision-making in EEG analysis

Abstract

Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorder analysis, and brain-computer interfaces. However, the effective analysis and interpretation of these complex signals often present significant challenges. This paper presents a novel approach that integrates computer graphics techniques with biological signal pattern recognition, specifically using Markov Transfer Fields (MTFs) for EEG time series imaging. The proposed framework (STEAM-EEG) employs the capabilities of MTFs to capture the spatiotemporal dynamics of EEG signals, transforming them into visually informative images. These images are then rendered, visualised, and modelled using state-of-the-art computer graphics techniques, thereby facilitating enhanced data exploration, pattern recognition, and decision-making. The code could be accessed from GitHub.