# Partial Label Learning for Emotion Recognition from EEG

arXiv: https://arxiv.org/abs/2302.13170 · HTML: https://mlsanity.com/paper/2302.13170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 72 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 27 of 27)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation](https://mlsanity.com/paper/2106.04732/) (2021)
- [Progressive Identification of True Labels for Partial-Label Learning](https://mlsanity.com/paper/2002.08053/) (2020)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Leveraged Weighted Loss for Partial Label Learning](https://mlsanity.com/paper/2106.05731/) (2021)
- [Incremental False Negative Detection for Contrastive Learning](https://mlsanity.com/paper/2106.03719/) (2021)
- [ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification](https://mlsanity.com/paper/1802.00308/) (2018)
- [Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface](https://mlsanity.com/paper/1708.06578/) (2017)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition](https://mlsanity.com/paper/2202.05400/) (2022)
- [Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation](https://mlsanity.com/paper/1912.07812/) (2019)
- [Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition](https://mlsanity.com/paper/2108.02241/) (2021)
- [Distilling EEG Representations via Capsules for Affective Computing](https://mlsanity.com/paper/2105.00104/) (2021)
- [Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2107.13505/) (2021)
- [On the Power of Deep but Naive Partial Label Learning](https://mlsanity.com/paper/2010.11600/) (2020)
- [Holistic Semi-Supervised Approaches for EEG Representation Learning](https://mlsanity.com/paper/2109.11732/) (2021)

## Built on by (most cited first, 3 of 3)

- [EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning](https://mlsanity.com/paper/2308.00246/) (2023)
- [Understanding Mental States in Active and Autonomous Driving with EEG](https://mlsanity.com/paper/2512.09190/) (2025)
- [Investigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning](https://mlsanity.com/paper/2512.11095/) (2025)

## Abstract (arXiv)

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.