# PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition

arXiv: https://arxiv.org/abs/2202.05400 · HTML: https://mlsanity.com/paper/2202.05400/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (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)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [How Does Mixup Help With Robustness and Generalization?](https://mlsanity.com/paper/2010.04819/) (2020)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation](https://mlsanity.com/paper/2106.04732/) (2021)
- [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)
- [Unsupervised Pretraining for Sequence to Sequence Learning](https://mlsanity.com/paper/1611.02683/) (2016)
- [A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition](https://mlsanity.com/paper/1906.01704/) (2019)
- [Spatial-Temporal Recurrent Neural Network for Emotion Recognition](https://mlsanity.com/paper/1705.04515/) (2017)
- [Semi-Supervised Learning by Augmented Distribution Alignment](https://mlsanity.com/paper/1905.08171/) (2019)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [On Data-Augmentation and Consistency-Based Semi-Supervised Learning](https://mlsanity.com/paper/2101.06967/) (2021)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis](https://mlsanity.com/paper/1908.05349/) (2019)
- [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)
- [Holistic Semi-Supervised Approaches for EEG Representation Learning](https://mlsanity.com/paper/2109.11732/) (2021)

## Built on by (most cited first, 12 of 12)

- [Label-efficient Time Series Representation Learning: A Review](https://mlsanity.com/paper/2302.06433/) (2023)
- [Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines](https://mlsanity.com/paper/2304.04273/) (2023)
- [EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2304.06496/) (2023)
- [Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition](https://mlsanity.com/paper/2308.11635/) (2023)
- [AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition](https://mlsanity.com/paper/2206.04625/) (2022)
- [A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface](https://mlsanity.com/paper/2209.03785/) (2022)
- [EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning](https://mlsanity.com/paper/2308.00246/) (2023)
- [BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision](https://mlsanity.com/paper/2309.12056/) (2023)
- [Partial Label Learning for Emotion Recognition from EEG](https://mlsanity.com/paper/2302.13170/) (2023)
- [NSSI-Net: A Multi-Concept GAN for Non-Suicidal Self-Injury Detection Using High-Dimensional EEG in a Semi-Supervised Framework](https://mlsanity.com/paper/2410.12159/) (2024)
- [Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper](https://mlsanity.com/paper/2306.03112/) (2023)
- [Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classification](https://mlsanity.com/paper/2511.12394/) (2025)

## Abstract (arXiv)

We propose PARSE, a novel semi-supervised architecture for learning strong EEG representations for emotion recognition. To reduce the potential distribution mismatch between the large amounts of unlabeled data and the limited amount of labeled data, PARSE uses pairwise representation alignment. First, our model performs data augmentation followed by label guessing for large amounts of original and augmented unlabeled data. This is then followed by sharpening of the guessed labels and convex combinations of the unlabeled and labeled data. Finally, representation alignment and emotion classification are performed. To rigorously test our model, we compare PARSE to several state-of-the-art semi-supervised approaches which we implement and adapt for EEG learning. We perform these experiments on four public EEG-based emotion recognition datasets, SEED, SEED-IV, SEED-V and AMIGOS (valence and arousal). The experiments show that our proposed framework achieves the overall best results with varying amounts of limited labeled samples in SEED, SEED-IV and AMIGOS (valence), while approaching the overall best result (reaching the second-best) in SEED-V and AMIGOS (arousal). The analysis shows that our pairwise representation alignment considerably improves the performance by reducing the distribution alignment between unlabeled and labeled data, especially when only 1 sample per class is labeled.