ArticleCyborg and bionic systems (Washington, D.C.)2024
A Domain Generalization and Residual Network-Based Emotion Recognition from Physiological Signals.
Article in Cyborg and bionic systems (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Emotional regulation and music engagement: a meta-analytic synthesis.Frontiers in psychology · 2026Pooled it
- Heatwave Exposure Accelerates Biological Aging via Metabolic Dysregulation.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- DSP-MCF: dual stream pre-training and multi-view consistency fine-tuning for cross-subject EEG emotion recognition.Frontiers in human neuroscience · 2026Article
- [Multi-source adversarial adaptation with calibration for electroencephalogram-based classification of meditation and resting states].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- A deep learning approach to stress recognition through multimodal physiological signal image transformation.Scientific reports · 2025Article
- Cross-Subject Emotion Recognition with CT-ELCAN: Leveraging Cross-Modal Transformer and Enhanced Learning-Classify Adversarial Network.Bioengineering (Basel, Switzerland) · 2025Article
- GNN-RMNet: Leveraging graph neural networks and GPS analytics for driver behavior and route optimization in logistics.PloS one · 2025Article
- Exploration of the Prognostic Role of Apoptosis-Related Genes in Glioblastoma.Stem cells international · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotion recognition from physiological signals (ERPS) has drawn tremendous attention and can be potentially applied to numerous fields. Since physiological signals are nonstationary time series with high sampling frequency, it is challenging to directly extract features from them. Additionally, there are 2 major challenges in ERPS: (a) how to adequately capture the correlations between physiological signals at different times and between different types of physiological signals and (b) how to effectively minimize the negative effect caused by temporal covariate shift (TCS). To tackle these problems, we propose a domain generalization and residual network-based approach for emotion recognition from physiological signals (DGR-ERPS). We first pre-extract time- and frequency-domain features from the original time series to compose a new time series. Then, in order to fully extract the correlation information of different physiological signals, these time series are converted into 3D image data to serve as input for a residual-based feature encoder (RBFE). In addition, we introduce a domain generalization-based technique to mitigate the issue posed by TCS. We have conducted extensive experiments on 2 real-world datasets, and the results indicate that our DGR-ERPS achieves superior performance under both TCS and non-TCS scenarios.
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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.