ArticleSensors (Basel, Switzerland)2019
Emotion Recognition from Multiband EEG Signals Using CapsNet.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.Frontiers in psychology · 2024Pooled it
- KTU-MEDAFE: A Newly Developed Multimodal Dataset for Emotion Recognition Using EEG-Speech Decision-Level Fusion.Sensors (Basel, Switzerland) · 2026Article
- Music Therapy in Depression: Exploring Mechanisms and Efficacy in Rat Models.Brain sciences · 2025Review
- An improved CapsNet based on data augmentation for driver vigilance estimation with forehead single-channel EEG.Cognitive neurodynamics · 2024Article
- CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition.Brain sciences · 2024Article
- Article
- Review
- MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition.Brain sciences · 2024Article
- FC-TFS-CGRU: A Temporal-Frequency-Spatial Electroencephalography Emotion Recognition Model Based on Functional Connectivity and a Convolutional Gated Recurrent Unit Hybrid Architecture.Sensors (Basel, Switzerland) · 2024Article
- ERTNet: an interpretable transformer-based framework for EEG emotion recognition.Frontiers in neuroscience · 2024Article
- Evaluation of grouped capsule network for intracranial hemorrhage segmentation in CT scans.Scientific reports · 2023Article
- Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain-Computer Interfaces.Brain sciences · 2023Article
- An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.Sensors (Basel, Switzerland) · 2023Article
- Deep learning-based EEG emotion recognition: Current trends and future perspectives.Frontiers in psychology · 2023Review
- Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network.Journal of healthcare engineering · 2023Article
- Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2022Article
- Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals.Entropy (Basel, Switzerland) · 2022Article
- Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals.Sensors (Basel, Switzerland) · 2022Article
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced to recognize emotion states according to the input MFM. Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models. The experimental results demonstrate that the three characteristics contained in the MFM were complementary and the capsule network was more suitable for mining and utilizing the three correlation characteristics.
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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.