用图像化方法分析脑电时空动态，提升疾病诊断与脑机接口性能。
STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs
- 将脑电信号转化为可视图像，结合马尔可夫转移场捕捉时空模式。
- 通过图形化处理增强脑电数据的可视化与模式识别能力。
- 适合神经科学、临床诊断及脑机接口研究者参考。
脑电图（EEG）在癫痫诊断、睡眠障碍分析和脑机接口等生物医学研究与临床应用中具有关键作用。然而，复杂脑电信号的有效分析与解释常面临巨大挑战。本文提出一种新方法，融合计算机图形技术与生物信号模式识别，采用马尔可夫转移场（MTFs）对脑电时间序列进行成像。所提出的框架（STEAM-EEG）利用MTFs捕捉脑电信号的时空动态，将其转化为具有视觉信息的图像。这些图像随后通过先进的计算机图形技术进行渲染、可视化与建模，从而促进数据探索、模式识别与决策支持。代码已开源至GitHub。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。