ArticleSensors (Basel, Switzerland)2020
EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.Biosensors · 2025Pooled it
- A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets.F1000Research · 2025Pooled it
- EEG and ECG Wearable Biosensor-Based Affective State Analysis Using Deep Learning over 6G IoT Healthcare Networks.Biosensors · 2026Article
- EED-CL: Extended EEG Deformer with Contrastive Learning for Robust Emotion Recognition.Bioengineering (Basel, Switzerland) · 2025Article
- Early Diagnosis of Alzheimer's Disease in Human Participants Using EEGConformer and Attention-Based LSTM During the Short Question Task.Diagnostics (Basel, Switzerland) · 2025Article
- Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism.Scientific reports · 2025Article
- Attention-Based PSO-LSTM for Emotion Estimation Using EEG.Sensors (Basel, Switzerland) · 2024Article
- Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.Brain sciences · 2024Article
- Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.Scientific reports · 2024Article
- Application of bidirectional long short-term memory network for prediction of cognitive age.Scientific reports · 2023Article
- EEG-Based Emotion Recognition with Consideration of Individual Difference.Sensors (Basel, Switzerland) · 2023Article
- Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest.Sensors (Basel, Switzerland) · 2022Article
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- Article
- Hemodynamic functional connectivity optimization of frequency EEG microstates enables attention LSTM framework to classify distinct temporal cortical communications of different cognitive tasks.Brain informatics · 2022Article
- Dual attentive fusion for EEG-based brain-computer interfaces.Frontiers in neuroscience · 2022Article
- EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.Sensors (Basel, Switzerland) · 2021Article
- Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees.Sensors (Basel, Switzerland) · 2021Article
- EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph.Sensors (Basel, Switzerland) · 2021Article
- Identification of Breathing Patterns through EEG Signal Analysis Using Machine Learning.Brain sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Recently, studies that analyze emotions based on physiological signals, such as electroencephalogram (EEG), by applying a deep learning algorithm have been actively conducted. However, the study of sequence modeling considering the change of emotional signals over time has not been fully investigated. To consider long-term interaction of emotion, in this study, we propose a long short-term memory network to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak-end rule in psychology. We used 32-channel EEG data from the DEAP database. Two-level (low and high) and three-level (low, middle, and high) classification experiments were performed on the valence and arousal emotion models. The results show accuracies of 90.1% and 87.9% using the two-level classification for the valence and arousal models with four-fold cross validation, respectively. In the case of the three-level classification, these values were obtained as 83.5% and 82.6%, respectively. Additional experiments were conducted using a network combining a convolutional neural network (CNN) submodule with the proposed model. The obtained results showed accuracies of 90.1% and 88.3% in the case of the two-level classification and 86.9% and 84.1% in the case of the three-level classification for the valence and arousal models with four-fold cross validation, respectively. In 10-fold cross validation, there were 91.8% for valence and 91.6% for arousal accuracy, respectively.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.