STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs
This work addresses the problem of interpreting EEG signals for biomedical research and clinical applications, such as epilepsy diagnosis and brain-computer interfaces, by introducing a novel integration of computer graphics with signal processing.
The paper tackles the challenge of analyzing complex EEG signals by proposing STEAM-EEG, a framework that uses Markov Transfer Fields to convert EEG data into images and applies computer graphics techniques for visualization and modeling, resulting in enhanced data exploration and pattern recognition.
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.