# Identifying Stable Patterns over Time for Emotion Recognition from EEG

arXiv: https://arxiv.org/abs/1601.02197 · HTML: https://mlsanity.com/paper/1601.02197/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2016.

## Built on by (most cited first, 30 of 43)

- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition](https://mlsanity.com/paper/2104.02935/) (2021)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface](https://mlsanity.com/paper/2105.02786/) (2021)
- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2108.03354/) (2021)
- [4D Attention-based Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2101.05484/) (2021)
- [MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition](https://mlsanity.com/paper/2308.16207/) (2023)
- [Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition](https://mlsanity.com/paper/2202.02901/) (2022)
- [Source-free Subject Adaptation for EEG-based Visual Recognition](https://mlsanity.com/paper/2301.08448/) (2023)
- [SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction](https://mlsanity.com/paper/2104.06308/) (2021)
- [ScalingNet: extracting features from raw EEG data for emotion recognition](https://mlsanity.com/paper/2105.13987/) (2021)
- [Holistic Semi-Supervised Approaches for EEG Representation Learning](https://mlsanity.com/paper/2109.11732/) (2021)
- [EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters](https://mlsanity.com/paper/2110.10009/) (2021)
- [Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models](https://mlsanity.com/paper/2407.20253/) (2024)
- [Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition](https://mlsanity.com/paper/2110.09955/) (2021)
- [M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition](https://mlsanity.com/paper/2404.15615/) (2024)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)
- [Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition](https://mlsanity.com/paper/2203.06895/) (2022)
- [EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition](https://mlsanity.com/paper/2308.05767/) (2023)
- [Semi-Supervised End-To-End Contrastive Learning For Time Series Classification](https://mlsanity.com/paper/2310.08848/) (2023)
- [DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis](https://mlsanity.com/paper/2407.20519/) (2024)
- [Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition](https://mlsanity.com/paper/2502.00547/) (2025)
- [CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition](https://mlsanity.com/paper/2504.09221/) (2025)
- [Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework](https://mlsanity.com/paper/2505.18175/) (2025)
- [Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games](https://mlsanity.com/paper/2103.03488/) (2021)
- [Recognizing Emotions evoked by Movies using Multitask Learning](https://mlsanity.com/paper/2107.14529/) (2021)
- [MutualGraphNet: A novel model for motor imagery classification](https://mlsanity.com/paper/2109.04361/) (2021)
- [A novel Fourier Adjacency Transformer for advanced EEG emotion recognition](https://mlsanity.com/paper/2503.13465/) (2025)
- [Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony](https://mlsanity.com/paper/2504.03643/) (2025)

## Abstract (arXiv)

In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.