arXiv Machine Learning June 10, 2026 By S M Rakib UI Karim, Diponkor Bala, Wenyi Lu, Rownak Ara Rasul, Sean Goggins

DAH-Net: A Dual-Attention Hybrid Network for Interpretable and Robust EEG-Based Emotion Recognition

Read the original on arXiv Machine Learning →

arXiv:2602. 06411v2 Announce Type: replace Abstract: EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity, subject variability, and limited interpretability.

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

arXiv AI

Aug 11

A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition

arXiv:2608. 09088v1 Announce Type: new Abstract: Mixed emotions represent a clinically relevant but still underexplored target for automatic emotion recognition.

By Stefanos Gkikas, Yang Guo, Guangliang Li, Raul Fernandez Rojas, Giorgos Giannakakis, Randy Gomez

arXiv Machine Learning

Jun 26

State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

arXiv:2606. 26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states.

By Andrei Velichko, Mehmet Tahir Huyut

arXiv AI

1d ago

MUPA$^{2}$E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment

arXiv:2608. 15999v1 Announce Type: new Abstract: Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelines before fusion.

By Stefanos Gkikas, Eric Nichols, Christian Arzate Cruz, Randy Gomez

multimodal

arXiv Machine Learning

Jul 23

Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation

arXiv:2504. 03707v2 Announce Type: replace-cross Abstract: Emotion recognition is crucial for advancing mental health, healthcare, and technologies such as brain-computer interfaces.

By Md Niaz Imtiaz, Naimul Khan

fine-tuning benchmarks safety

arXiv AI

Jun 10

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

llms benchmarks

arXiv Machine Learning

Jun 2

OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models

arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.

By Ziling Lu, Zongsheng Li, Xinke Shen, Kexin Lou, Yingyue Xin, Xiaoqi Chen, Shinan Wang, Xiang Chen, Jiahao Fan, Chenyu Huang, Xin Xu, Zhoujie Hou, Chen Wei, Quanying Liu

llms benchmarks

Related stories

A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition

State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

MUPA$^{2}$E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment

Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation

Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition

OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models