arXiv:2608.24727v1 Announce Type: cross
Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...
By Meghal Dani, Stefanie Liebe
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tun...
NeuroAtlas is the largest EEG benchmark to date, comprising 42 datasets and 260,000 hours of clinical EEG data across epilepsy, sleep medicine, brain age estimation, and brain‑computer interfaces. The study evaluates foundation models (FMs) for EEG against supervised baselines and generic time‑series FMs, finding that EEG‑specific FMs do not consistently outperform generic ones. It also demonstrates that standard machine‑learning metrics are inadequate for clinical relevance, advocating for task‑specific measures such as event‑level decision quality, hypnogram features, and brain‑age gap.
By Konstantinos Kontras, Trui Osselaer, Stylianos G. Mouslech, Angeliki-Ilektra Karaiskou, Guido Gagliardi, Thomas Strypsteen, Mohammad Hossein Badiei, Anku Rani, Maarten Vanmarcke, Miguel Bhagubai, Chanakya Ekbote, Jaedong Hwang, Christos Chatzichristos, Paul Pu Liang, Maarten De Vos
arXiv:2609.36609v1 Announce Type: cross
Abstract: Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are h...
By Parsa Razmara, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, Takfarinas Medani
arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.
By Siyang Li, Jiayi Ouyang, Zhenyao Cui, Ziwei Wang, Tianwang Jia, Feng Wan, Dongrui Wu
Brain4FMs is a unified benchmark for evaluating brain foundation models (BFMs) on both scalp EEG and intracranial EEG (iEEG). It incorporates 17 representative models and 21 public datasets spanning clinical diagnosis, sleep staging, communication, and affective computing, and offers dataset-aware preprocessing, cross‑subject evaluation, and standardized downstream workflows. The benchmark highlights that no single BFM consistently outperforms others across all tasks, modalities, and adaptation protocols, prompting further exploratory analyses of model‑specific spatial, spectral, and discrete representations.
By Fanqi Shen, Enhong Yang, Jiahe Li, Junru Hong, Xiaoran Pan, Zhizhang Yuan, Meng Li, Yang Yang