Data-Centric AI for EEG-Based Emotion Recognition: Noise Filtering and Augmentation Strategies

TL;DR

This paper presents a data-focused approach to improve emotion recognition from EEG data by enhancing data quality through noise filtering and augmentation.

Contribution

The novel contribution is a data-centric framework that improves EEG emotion recognition by prioritizing data quality over complex models.

Findings

Participant-guided noise filtering and data augmentation improve classification performance across multiple emotion settings.

The proposed strategies achieve competitive accuracy and F1 scores compared to more complex models using the SEED-VII dataset.

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.

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