arXiv AI

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.

arXiv AI

Jul 24

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

arXiv:2607. 21402v1 Announce Type: new Abstract: Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis.

By Tao Zhou, Jing Han, Lingyu Shu, Zixing Zhang

nlp efficiency benchmarks

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

Jul 13

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

llms 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 Machine Learning

Aug 4

SingLEM: Single-Channel Large EEG Model

arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.

By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka

llms

arXiv AI

Aug 10

ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?

arXiv:2608. 07033v1 Announce Type: new Abstract: This work investigates whether Electroencephalograph (EEG) foundation models (EFMs) can be made faster and locally deployable without sacrificing accuracy.

By Lingwei Li, Yirong Kan, Peng Chen, Xu Cao, Zheng Chen, Yasuhiko Nakashima

llms

arXiv Machine Learning

Jul 7

miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models

arXiv:2409. 07589v2 Announce Type: cross Abstract: EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces.

By Xin Zhou, Dawei Huang, Xiaojing Peng, Lijun Yin

benchmarks

arXiv AI

Jun 30

Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model

arXiv:2606. 30104v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings.

By Ay\c{s}e Bet\"ul Y\"uce, Chris Joey Leffler, Sarun Varghese, Myra Spiliopoulou, Sebastian Stober

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

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 2

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.

By Xin Zhou, Xiang Zhang, Hao Deng, Lijun Yin

fine-tuning benchmarks safety

arXiv AI

Jul 24

Multimodal Pretraining for Generalizable EEG Representation Learning

arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.

By Targol Bakhtiarvand, Jugal Kalita, Adham Atyabi

llms rag computer-vision fine-tuning multimodal benchmarks safety

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