Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception

• URL: http://arxiv.org/abs/2508.11691v1
• Date: Tue, 12 Aug 2025 09:57:32 GMT
• Title: Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception
• Authors: Mathis Rezzouk, Fabrice Gagnon, Alyson Champagne, Mathieu Roy, Philippe Albouy, Michel-Pierre Coll, Cem Subakan,
• Abstract summary: We systematically evaluate the performance of cross-participant generalization of a wide range of machine learning models.<n>Traditional models suffered the largest drop from within- to cross-participant performance, while deep learning models proved more resilient.<n>Even though performance variability remained high, the strong results of the graph-based model highlight its potential to capture subject-invariant structure in EEG signals.
• Score: 1.718323575065371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EEG-based analysis of pain perception, enhanced by machine learning, reveals how the brain encodes pain by identifying neural patterns evoked by noxious stimulation. However, a major challenge that remains is the generalization of machine learning models across individuals, given the high cross-participant variability inherent to EEG signals and the limited focus on direct pain perception identification in current research. In this study, we systematically evaluate the performance of cross-participant generalization of a wide range of models, including traditional classifiers and deep neural classifiers for identifying the sensory modality of thermal pain and aversive auditory stimulation from EEG recordings. Using a novel dataset of EEG recordings from 108 participants, we benchmark model performance under both within- and cross-participant evaluation settings. Our findings show that traditional models suffered the largest drop from within- to cross-participant performance, while deep learning models proved more resilient, underscoring their potential for subject-invariant EEG decoding. Even though performance variability remained high, the strong results of the graph-based model highlight its potential to capture subject-invariant structure in EEG signals. On the other hand, we also share the preprocessed dataset used in this study, providing a standardized benchmark for evaluating future algorithms under the same generalization constraints.

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