arXiv Machine Learning July 7, 2026 By Huqin Weng, Jiayang Huang, Yimin Wen, Jie Du, Chi-Man Vong, Chuangquan Chen

Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition

Read the original on arXiv Machine Learning →

arXiv:2607. 04139v1 Announce Type: new Abstract: Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Dive into Waves: Morlet Spectral Transformer for Cross-Subject Emotion Decoding from EEG

arXiv:2606. 00884v1 Announce Type: cross Abstract: We study cross-subject emotion recognition from EEG, a practically important yet challenging problem in brain-computer interfaces.

By Jiaxin Qing, Lexin Li

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CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding

arXiv:2607. 09543v1 Announce Type: new Abstract: Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications.

By Gabriel Mahuas, Victoria Shevchenko, Ugo Tanielian, Yassir Bendou, Richard Gao

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EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction

arXiv:2606. 05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals.

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RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning

arXiv:2606. 15278v1 Announce Type: cross Abstract: Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time, challenging robust representation learning from EEG/sEEG for clinical diagnosis.

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Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition

arXiv:2606. 10718v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness.

By Xinglong Cui, Dian Gu

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MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

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Dive into Waves: Morlet Spectral Transformer for Cross-Subject Emotion Decoding from EEG

CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding

EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction

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MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning