Partial Label Learning for Emotion Recognition from EEG

TL;DR

This paper explores the adaptation of Partial Label Learning (PLL) methods for emotion recognition from EEG data, demonstrating that PLL can perform comparably to fully supervised methods and is effective in real-world affective computing scenarios.

Contribution

The study adapts six state-of-the-art PLL approaches for EEG-based emotion recognition, evaluating their performance on large datasets and analyzing the impact of label disambiguation.

Findings

PLL methods achieve strong results comparable to fully supervised learning.

Label disambiguation benefits models when candidate labels are similar to ground truth.

PLL is effective for real-world affective EEG tasks.

Abstract

Fully supervised learning has recently achieved promising performance in various electroencephalography (EEG) learning tasks by training on large datasets with ground truth labels. However, labeling EEG data for affective experiments is challenging, as it can be difficult for participants to accurately distinguish between similar emotions, resulting in ambiguous labeling (reporting multiple emotions for one EEG instance). This notion could cause model performance degradation, as the ground truth is hidden within multiple candidate labels. To address this issue, Partial Label Learning (PLL) has been proposed to identify the ground truth from candidate labels during the training phase, and has shown good performance in the computer vision domain. However, PLL methods have not yet been adopted for EEG representation learning or implemented for emotion recognition tasks. In this paper, we adapt and re-implement six state-of-the-art PLL approaches for emotion recognition from EEG on two large emotion datasets (SEED-IV and SEED-V). These datasets contain four and five categories of emotions, respectively. We evaluate the performance of all methods in classical, circumplex-based and real-world experiments. The results show that PLL methods can achieve strong results in affective computing from EEG and achieve comparable performance to fully supervised learning. We also investigate the effect of label disambiguation, a key step in many PLL methods. The results show that in most cases, label disambiguation would benefit the model when the candidate labels are generated based on their similarities to the ground truth rather than obeying a uniform distribution. This finding suggests the potential of using label disambiguation-based PLL methods for circumplex-based and real-world affective tasks.