EEG-based Emotional Valence and Emotion Regulation Classification: A Data-centric and Explainable Approach

Abstract

Abstract Emotion classification using electroencephalographic (EEG) data is a challenging task in the field of Artificial Intelligence. While many researchers have focused on finding the best model or feature extraction technique to achieve optimal results, few have attempted to select the best methodological steps for working with the dataset. In this study, we applied two different theoretical approaches based on the noise of the dataset: curriculum learning and confident learning. Curriculum learning involves presenting training examples to a machine learning model in a specific order, starting with easier examples and gradually increasing in difficulty. This approach has been shown to improve model performance. Confident learning, on the other hand, is a method for identifying and correcting label errors in datasets. By identifying and correcting these errors, confident learning can improve the performance of machine learning models trained on noisy datasets. Our aim was to explore the impact of different models and methods on emotion classification performance using EEG data. We used an EEG dataset in which participants rated the emotional valence of pictures while performing an emotion regulation (ER) task, comparing a control condition (Look) with two ER strategies: cognitive reappraisal and expressive suppression. We performed a multilabel classification to identify emotional neutrality or polarization of emotional valence (positive or negative) rated by participants and the emotion regulation strategy adopted during the task. We compared the performance of models trained on three datasets selected based on label noise and evaluated their suitability for this task. We then applied the Integrated Gradient technique to each model in order to assess the explainability of each model. Our results suggest different patterns based on the architecture used for feature importance, highlighting both advantages and criticisms.