ArticleFrontiers in neuroscience2021
Multi-Source Co-adaptation for EEG-Based Emotion Recognition by Mining Correlation Information.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prototypical graph based deep label propagation with semantic augmentation for cross-subject and cross-session EEG emotion recognition.Frontiers in neuroscience · 2026Article
- Domain adaptive deep possibilistic clustering for EEG-based emotion recognition.Frontiers in neuroscience · 2025Article
- Discriminative possibilistic clustering promoting cross-domain emotion recognition.Frontiers in neuroscience · 2024Article
- Possibilistic distribution distance metric: a robust domain adaptation learning method.Frontiers in neuroscience · 2023Article
- A regression method for EEG-based cross-dataset fatigue detection.Frontiers in physiology · 2023Article
- Local domain generalization with low-rank constraint for EEG-based emotion recognition.Frontiers in neuroscience · 2023Article
- Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition.Frontiers in neuroscience · 2022Article
- Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization.Frontiers in computational neuroscience · 2021Article
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Authors and funding
2 authors.
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Abstract
Since each individual subject may present completely different encephalogram (EEG) patterns with respect to other subjects, existing subject-independent emotion classifiers trained on data sampled from cross-subjects or cross-dataset generally fail to achieve sound accuracy. In this scenario, the domain adaptation technique could be employed to address this problem, which has recently got extensive attention due to its effectiveness on cross-distribution learning. Focusing on cross-subject or cross-dataset automated emotion recognition with EEG features, we propose in this article a robust multi-source co-adaptation framework by mining diverse correlation information (MACI) among domains and features with
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