Document Type
Article
Publication Date
11-18-2025
Journal Title
Bioengineering
Volume Number
12
Issue Number
11
First Page
1264
DOI
https://doi.org/10.3390/bioengineering12111264
Version
Publisher PDF: the final published version of the article, with professional formatting and typesetting
Creative Commons License
This work is licensed under a CC BY License.
Disciplines
Engineering
Abstract
Research in the biomedical field often faces challenges due to the scarcity and high cost of data, which significantly limit the development and application of machine learning models. This paper introduces a data-centric AI framework for EEG-based emotion recognition that emphasizes improving data quality rather than model complexity. Instead of proposing a deep architecture, we demonstrate how participant-guided noise filtering combined with systematic data augmentation can substantially enhance system performance across multiple classification settings: binary (high vs. low arousal), four-quadrant emotions, and seven discrete emotions. Using the SEED-VII dataset, we show that these strategies consistently improve accuracy and F1 scores, achieving competitive or superior performance compared to more sophisticated published models. The findings highlight a practical and reproducible pathway for advancing biomedical AI systems, showing that prioritizing data quality over architectural novelty yields robust and generalizable improvements in emotion recognition.
Digital USD Citation
Moghadam, Nadieh and Hegazy, Rana, "Data-Centric AI for EEG-Based Emotion Recognition: Noise Filtering and Augmentation Strategies" (2025). School of Engineering: Faculty Scholarship. 46.
https://digital.sandiego.edu/engineering_facpub/46
Notes
Original article available: Bioengineering 2025, 12(11), 1264; https://doi.org/10.3390/bioengineering12111264