mlsanityData to 14 Oct 2026

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition

arXiv 2607.00358 ↗First posted 1 Jul 2026cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Electroencephalogram (EEG) captures endogenous brain activity with high temporal fidelity and holds substantial promise for precise emotion decoding. However, channel redundancy and pronounced inter-subject variability remain key obstacles to scalable generalization. To address these limitations, we propose a novel framework termed PRioritized channel Importance with Semi-supervised doMain adaptation (PRISM), enabling label-efficient cross-subject emotion decoding. On the channel side, PRISM assigns differentiable, data-dependent channel weights via a lightweight expert ensemble, amplifying reliable electrodes while suppressing distractors. On the domain side, PRISM leverages unlabeled data through confidence-filtered pseudo-labels to drive consistency regularization and domain alignment, mitigating subject-specific heterogeneity. Extensive experiments show that PRISM surpasses state-of-the-art methods on DEAP, DREAMER, and SEED datasets, achieving robust cross-subject generalization given limited annotations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 56 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

33 of 56 references.

References · 33

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  5. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  6. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,631
  7. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  8. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  9. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  10. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  11. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  12. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
21 more
  1. 2021FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelingcited by 540
  2. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  3. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 287
  4. 2013Learning Factored Representations in a Deep Mixture of Expertscited by 278
  5. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  6. 2019Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Modelscited by 178
  7. 2023SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learningcited by 133
  8. 2021AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptationcited by 104
  9. 2024Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecastingcited by 104
  10. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  11. 2022MAtt: A Manifold Attention Network for EEG Decodingcited by 44
  12. 2023Boosting Semi-Supervised Learning by Exploiting All Unlabeled Datacited by 26
  13. 2024EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognitioncited by 24
  14. 2023EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognitioncited by 10
  15. 2023Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognitioncited by 10
  16. 2025Learning Soft Sparse Shapes for Efficient Time-Series Classificationcited by 10
  17. 2021Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networkscited by 6
  18. 2024AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learningcited by 3
  19. 2022Unsupervised Time-Aware Sampling Network with Deep Reinforcement Learning for EEG-Based Emotion Recognitioncited by 2
  20. 2024miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Modelscited by 2
  21. 2024Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognitioncited by 1

Built on by

Not cited yet.