Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition
This addresses label inconsistency issues in EEG-based emotion recognition, offering improved generalization and explainability for applications in affective computing, though it is incremental as it builds on existing regularization and graph theory methods.
The paper tackles the problem of Timescale Dependent Label Inconsistency in EEG emotion recognition by proposing Local Variation Loss and Local-Global Consistency Loss regularization strategies, which outperform state-of-the-art baselines on datasets like DREAMER and DEAP, with LVL achieving the best aggregate rank across metrics.
In this work, we address the often-overlooked issue of Timescale Dependent Label Inconsistency (TsDLI) in training neural network models for EEG-based human emotion recognition. To mitigate TsDLI and enhance model generalization and explainability, we propose two novel regularization strategies: Local Variation Loss (LVL) and Local-Global Consistency Loss (LGCL). Both methods incorporate classical mathematical principles--specifically, functions of bounded variation and commute-time distances--within a graph theoretic framework. Complementing our regularizers, we introduce a suite of new evaluation metrics that better capture the alignment between temporally local predictions and their associated global emotion labels. We validate our approach through comprehensive experiments on two widely used EEG emotion datasets, DREAMER and DEAP, across a range of neural architectures including LSTM and transformer-based models. Performance is assessed using five distinct metrics encompassing both quantitative accuracy and qualitative consistency. Results consistently show that our proposed methods outperform state-of-the-art baselines, delivering superior aggregate performance and offering a principled trade-off between interpretability and predictive power under label inconsistency. Notably, LVL achieves the best aggregate rank across all benchmarked backbones and metrics, while LGCL frequently ranks the second, highlighting the effectiveness of our framework.