Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception
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arXiv
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| Format: | Preprint |
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2025
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| author | Rezzouk, Mathis Gagnon, Fabrice Champagne, Alyson Roy, Mathieu Albouy, Philippe Coll, Michel-Pierre Subakan, Cem |
| author_facet | Rezzouk, Mathis Gagnon, Fabrice Champagne, Alyson Roy, Mathieu Albouy, Philippe Coll, Michel-Pierre Subakan, Cem |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception Rezzouk, Mathis Gagnon, Fabrice Champagne, Alyson Roy, Mathieu Albouy, Philippe Coll, Michel-Pierre Subakan, Cem Signal Processing Artificial Intelligence Machine Learning 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. |
| title | Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.11691 |