ReviewFrontiers in systems neuroscience2021
Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review.
Review in Frontiers in systems neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Comprehensive survey on multimodal emotion detection with advances, challenges, and future directions in pairwise conversations.Cognitive processing · 2026Review
- EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.Scientific reports · 2024Article
- CATM: A Multi-Feature-Based Cross-Scale Attentional Convolutional EEG Emotion Recognition Model.Sensors (Basel, Switzerland) · 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
- EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism.Frontiers in computational neuroscience · 2024Article
- EEG-Based Emotion Recognition with Consideration of Individual Difference.Sensors (Basel, Switzerland) · 2023Article
- A Bimodal Emotion Recognition Approach through the Fusion of Electroencephalography and Facial Sequences.Diagnostics (Basel, Switzerland) · 2023Article
- An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG.Sensors (Basel, Switzerland) · 2023Article
- Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition.Brain sciences · 2022Article
- Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people's emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject's emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016-2021.
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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.