在EEG领域一般解释的平均LIME

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Izabela Rejer¹, Izabela Gago¹

¹West Pomeranian University of Technology in Szczecin, Żołnierska 49, Szczecin, 71-210, Poland.

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|November 15, 2025

概括

平均LIME通过平均局部解释来增强对像脑电图 (EEG) 这样的稳定系统中卷积神经网络 (CNN) 决策的理解. 这种方法揭示了隐藏的模式,以提高诊断中的深度学习透明度.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
人工智能的人工智能

背景情况:

像卷积神经网络 (CNN) 这样的深度学习模型往往缺乏透明度.
解释CNN决策对于神经信息学和诊断的应用至关重要.
解释CNN的现有方法可能无法提供可概括的见解,特别是对于具有稳定的空间分布的数据.

研究的目的:

引入平均LIME,这是一种用于全球解释CNN决策的新方法.
为了能够更好地理解在具有相对稳定的空间分布的系统中的CNN行为,例如基于事件相关潜力 (ERP) 的系统.
评估平均LIME在电脑电图 (EEG) 数据中的正确性和模式检测能力.

主要方法:

通过从Local Interpretable Model-agnostic Explanations (LIME) 算法中平均局部解释,开发了平均LIME.
通过两项研究评估LIME的平均值:一个是已知的感兴趣区域 (ROI) 和另一个探索性研究.
与SHapley添加式扩展 (SHAP) 和梯度加权类激活映射 (Grad-CAM) 进行了平均LIME的比较.

主要成果:

平均LIME在确定EEG数据中已知的ROI方面表现出可靠性.
该方法揭示了在原始数据或单个突出性地图中看不到的医学上可解释的ROI.
平均LIME产生的激活模式比SHAP和Grad-CAM更一致和更容易泛化.
成功地揭示了EEG数据中隐藏的一般模式.

结论:

averagedLIME增强了对具有稳定的空间分布的系统的CNN决策的全球解释.
与现有技术相比,该方法提供了更一致和更可普遍的激活模式.
在神经信息学和诊断应用中,averagedLIME具有显著的潜力,可以提高深度学习模型的透明度.

关键词:

平均的LIME时间在美国,CNN是CNN.这是一个EEGEEGEEGEEGEEGEEGEEG.可以解释性 (xAI)这是GRAD-CAM.在 LIME 时代,这就是 SHAP SHAP 的意思.

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