Machine Learning Classification of Active Viewing of Pain and Non-Pain Images Using EEG Does Not Exceed Chance in External Validation Samples

Tyler Mari,S. Hasan Ali, Lucrezia Pacinotti, Sarah Powsey,Nicholas Fallon

Cognitive, Affective, & Behavioral Neuroscience（2025）

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Abstract

Previous research has demonstrated that machine learning (ML) could not effectively decode passive observation of neutral versus pain photographs by using electroencephalogram (EEG) data. Consequently, the present study explored whether active viewing, i.e., requiring participant engagement in a task, of neutral and pain stimuli improves ML performance. Random forest (RF) models were trained on cortical event-related potentials (ERPs) during a two-alternative forced choice paradigm, whereby participants determined the presence or absence of pain in photographs of facial expressions and action scenes. Sixty-two participants were recruited for the model development sample. Moreover, a within-subject temporal validation sample was collected, consisting of 27 subjects. In line with our previous research, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The results demonstrated that the RF successfully classified discrete categories of visual stimuli (faces and scenes) with accuracies of 78

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Key words

Empathy,Electroencephalography,Event-related potential,Random forest,Faces

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【要点】：该研究探讨了在主动观察条件下，利用EEG数据通过机器学习对疼痛和非疼痛图像进行分类，发现其性能并未超过随机水平。

【方法】：研究使用了随机森林模型，在两选一强制选择范式下，对参与者的皮质事件相关电位（ERP）进行训练，以区分不同类型的视觉刺激。

【实验】：62名参与者被招募用于模型开发样本，27名参与者用于内部时间验证样本。实验使用了ERP数据，三个随机森林模型被开发用于分类图像，结果显示模型在分类视觉刺激方面有一定准确性，但在外部验证样本上对疼痛共情分类的性能并未超过随机水平。