ArticleSensors (Basel, Switzerland)2022
Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets.F1000Research · 2025Pooled it
- New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Adaptive frequency band attention-guided CNN-BiLSTM for Spatial-Spectral-Temporal EEG emotion recognition.Brain informatics · 2026Article
- An IoT-based smart emotion recognition system by using internal body parameters.Scientific reports · 2026Article
- Deep Hybrid CNN-BiLSTM-Attention Model for EEG Classification Using Wavelet Features.Annals of neurosciences · 2026Article
- Deep Learning-Based EEG Emotion Recognition: A Review.Brain sciences · 2025Review
- Comparative study of multi-headed and baseline deep learning models for ADHD classification from EEG signals.Physical and engineering sciences in medicine · 2025Article
- GBV-Net: Hierarchical Fusion of Facial Expressions and Physiological Signals for Multimodal Emotion Recognition.Sensors (Basel, Switzerland) · 2025Article
- Comparison of time-series models for predicting physiological metrics under sedation.Journal of clinical monitoring and computing · 2025Article
- Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.Brain and behavior · 2025Review
- Deep learning for electroencephalography emotion recognition.AIMS public health · 2025Article
- Multimodal modeling of emotion regulation in interactive art therapy: EEG and PANAS-based dynamic simulation.Frontiers in psychology · 2025Article
- CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition.Brain sciences · 2024Article
- Review
- Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.Scientific reports · 2024Article
- [Research progress on emotion recognition by combining virtual reality environment and electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Facial Expression Recognition for Measuring Jurors' Attention in Acoustic Jury Tests.Sensors (Basel, Switzerland) · 2024Article
- EEG-based emotion recognition using a temporal-difference minimizing neural network.Cognitive neurodynamics · 2024Article
- MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition.Brain sciences · 2024Article
- STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain-Computer Interface (BCI), to provide better human-machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.