arXiv Machine Learning

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

arXiv:2607. 00358v1 Announce Type: new Abstract: Electroencephalogram (EEG) captures endogenous brain activity with high temporal fidelity and holds substantial promise for precise emotion decoding.

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

Jul 1

A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification

arXiv:2602. 10528v2 Announce Type: replace-cross Abstract: We propose a novel swap-adversarial framework that mitigates high inter-subject variability and the high-dimensional low-sample-size problem in electrocorticography (ECoG) data.

By Seongwon Jin, Hanseul Choi, Sunggu Yang, Sungho Park, Jibum Kim

benchmarks safety

arXiv AI

Jun 2

UF-AMA: A unified framework for cross-domain emotion recognition via adaptive multimodal alignment

arXiv:2606. 00170v1 Announce Type: cross Abstract: In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions.

By Zheng Wang, Shuo Wang, Junhong Wang

llms fine-tuning efficiency multimodal benchmarks safety

arXiv Machine Learning

Jun 24

Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding

arXiv:2606. 23706v1 Announce Type: cross Abstract: The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health.

By Baimam Boukar Jean Jacques, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe, Kipngeno Koech

llms fine-tuning benchmarks

arXiv Machine Learning

Aug 4

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models

arXiv:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.

By Dingkun Liu, Yuheng Chen, Zhu Chen, Zhenyao Cui, Yaozhi Wen, Jiayu An, Jingwei Luo, Dongrui Wu

fine-tuning benchmarks

arXiv Machine Learning

Jul 7

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

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

By Huqin Weng, Jiayang Huang, Yimin Wen, Jie Du, Chi-Man Vong, Chuangquan Chen

rag fine-tuning

arXiv AI

5d ago

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding

arXiv:2608. 13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology.

By Shuailei Zhang, Muyun Jiang, Wei Zhang, Jinbo Chen, Zhiwei Guo, Yong Li, Yi Ding, Cuntai Guan

rag fine-tuning benchmarks

arXiv Machine Learning

Jul 7

Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding

arXiv:2607. 03094v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effective cross-subject generalization.

By Aymen Sarhane, Fouad Lbakali, Mouad Souissi, Jonathan Lys, Giulia Lioi

fine-tuning benchmarks

arXiv AI

Jun 2

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

llms nlp

arXiv Machine Learning

Aug 6

STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding

arXiv:2608. 02070v2 Announce Type: replace-cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.

By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu

safety

arXiv Machine Learning

Aug 4

STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding

arXiv:2608. 02070v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.

By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu

safety

arXiv Machine Learning

Aug 11

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding

arXiv:2506. 19141v3 Announce Type: replace-cross Abstract: Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task.

By Bruno Aristimunha, Dung Truong, Pierre Guetschel, Seyed Yahya Shirazi, Isabelle Guyon, Alexandre R. Franco, Michael P. Milham, Aviv Dotan, Scott Makeig, Alexandre Gramfort, Jean-Remi King, Marie-Constance Corsi, Pedro A. Vald\'es-Sosa, Amit Majumdar, Alan Evans, Terrence J Sejnowski, Oren Shriki, Sylvain Chevallier, Arnaud Delorme

Related stories