arXiv Machine Learning July 7, 2026 By Haofan Cheng, Jingjing Hu, Jingrong Pei, Shuaiqi Fu, Meilun Shen, Shuai Fang, Meng Wang, Dan Guo, Jie Zhang

A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction

Read the original on arXiv Machine Learning →

arXiv:2607. 02670v1 Announce Type: new Abstract: Electroencephalography (EEG) offers a noninvasive approach for examining neurophysiological correlates of dimensional psychopathology, yet systematic evidence across EEG paradigms and feature granularities remains limited.

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

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

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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

arXiv AI

Jun 16

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.

By Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu, Changjun Jiang

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 3

Assessing Region-Level EEG Contributions to Cognitive Workload Prediction

arXiv:2606. 02598v1 Announce Type: new Abstract: Accurate and generalizable estimation of cognitive workload from electroencephalography (EEG) is critical for human-centered and safety-critical systems.

By Jacob Wong, Sohan Singh, Prannaya Gupta, Jin Xing Ang, Kritika Johari, U-Xuan Tan

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arXiv Machine Learning

2d ago

EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models

arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.

By Deeksha M Shama, Punnisa Amornsirikul, Archana Venkataraman

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arXiv Machine Learning

Jul 9

STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning

arXiv:2607. 06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and psychiatric burden, and EEG is an attractive substrate for it because it is cheap, portable, and temporally rich.

By Roy Segal, Yoni Svechinsky, Tomer Fekete

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Related stories

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

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

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

Assessing Region-Level EEG Contributions to Cognitive Workload Prediction

EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models

STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning