# Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition

arXiv: https://arxiv.org/abs/2107.13505 · HTML: https://mlsanity.com/paper/2107.13505/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average](https://mlsanity.com/paper/1806.05594/) (2018)
- [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)
- [Pre-training strategies and datasets for facial representation learning](https://mlsanity.com/paper/2103.16554/) (2021)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning](https://mlsanity.com/paper/1908.00385/) (2019)

## Built on by (most cited first, 6 of 6)

- [Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines](https://mlsanity.com/paper/2304.04273/) (2023)
- [PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition](https://mlsanity.com/paper/2202.05400/) (2022)
- [Holistic Semi-Supervised Approaches for EEG Representation Learning](https://mlsanity.com/paper/2109.11732/) (2021)
- [Partial Label Learning for Emotion Recognition from EEG](https://mlsanity.com/paper/2302.13170/) (2023)
- [An embedding for EEG signals learned using a triplet loss](https://mlsanity.com/paper/2304.06495/) (2023)
- [Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2402.09438/) (2024)

## Abstract (arXiv)

EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often expensive and time-consuming. To tackle this problem and reduce the need for output labels in the context of EEG-based emotion recognition, we propose a semi-supervised pipeline to jointly exploit both unlabeled and labeled data for learning EEG representations. Our semi-supervised framework consists of both unsupervised and supervised components. The unsupervised part maximizes the consistency between original and reconstructed input data using an autoencoder, while simultaneously the supervised part minimizes the cross-entropy between the input and output labels. We evaluate our framework using both a stacked autoencoder and an attention-based recurrent autoencoder. We test our framework on the large-scale SEED EEG dataset and compare our results with several other popular semi-supervised methods. Our semi-supervised framework with a deep attention-based recurrent autoencoder consistently outperforms the benchmark methods, even when small sub-sets (3%, 5% and 10%) of the output labels are available during training, achieving a new state-of-the-art semi-supervised performance.