ArticleFrontiers in neuroscience2021
Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- Enhanced EEG Emotion Recognition Using MIMO-Based Denoising and Band-Wise Attention Graph Neural Network.Sensors (Basel, Switzerland) · 2026Article
- Article
- Transformer-based deep learning model for predicting fNIRS short-channel signals.Neurophotonics · 2025Article
- An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.Brain informatics · 2025Article
- Exploring physical and functional EEG connectivity with multilayer graph transformer convolutional networks for emotion recognition.Frontiers in human neuroscience · 2025Article
- Enhancing cross-subject emotion recognition precision through unimodal EEG: a novel emotion preceptor model.Brain informatics · 2024Article
- Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques.Scientific reports · 2024Article
- EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.Sensors (Basel, Switzerland) · 2024Article
- Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction.Brain sciences · 2024Article
- Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG.IEEE open journal of engineering in medicine and biology · 2024Article
- A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.Frontiers in physiology · 2024Article
- Cross-Sensory EEG Emotion Recognition with Filter Bank Riemannian Feature and Adversarial Domain Adaptation.Brain sciences · 2023Article
- EEG Emotion Recognition by Fusion of Multi-Scale Features.Brain sciences · 2023Article
- Exploiting social graph networks for emotion prediction.Scientific reports · 2023Article
- Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module.Sensors (Basel, Switzerland) · 2023Article
- Hybrid transfer learning strategy for cross-subject EEG emotion recognition.Frontiers in human neuroscience · 2023Article
- EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features.Sensors (Basel, Switzerland) · 2022Article
- Emotion Recognition With Knowledge Graph Based on Electrodermal Activity.Frontiers in neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.
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.