EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach.

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 the 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 is a method for identifying and correcting label errors in datasets. By identify

SUBMITTER: Fiorini L 

PROVIDER: S-EPMC11473962 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature