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

TL;DR

This paper evaluates the generalization of machine learning models, including deep neural networks, for EEG-based pain perception identification across individuals, highlighting the potential of graph-based models and providing a benchmark dataset.

Contribution

It systematically benchmarks various models on a novel EEG dataset for pain perception, emphasizing deep learning's resilience and introducing a standardized dataset for future research.

Findings

Deep learning models outperform traditional classifiers in cross-participant tasks.

Graph-based models show strong potential for subject-invariant EEG decoding.

Performance variability remains high across models and subjects.

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.