Neural State Prediction (NSP) is a latent‑predictive framework designed to curb shortcut learning in EEG foundation models. By using a target encoder updated with an exponential moving average, identity residualization, and topology‑separated context, NSP constrains both the prediction target and the available context. Trained on 2.2 million EEG segments, NSP outperforms baselines on 14 datasets in the EEG‑FM‑Bench, achieving 63.94 % macro balanced accuracy.
By Kieren Yu, Ziyang Liu, Chang Huang, Jintai Chen, Kaishun Wu
arXiv:2604. 16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations.
By Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun
arXiv:2609.36087v1 Announce Type: cross
Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
By Yipeng Zhang, Chenda Duan, Yuanyi Ding, Tianyi Wang, Atsuro Daida, Masaki Izumi, Yuta Tanoue, Naoto Kuroda, Shaun A. Hussain, Nishant Sinha, Eishi Asano. Hiroki Nariai, Vwani Roychowdhury
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
arXiv:2609.22088v1 Announce Type: cross
Abstract: Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in red...
By Beining Cao, Ziyi Zhao, Xiaowei Jiang, Daniel Leong, Yingtao Ren, Thomas Do, Yu-Cheng Fred Chang, Chin-Teng Lin
arXiv:2408. 12792v2 Announce Type: replace Abstract: Event detection turns long recordings into a sparse set of ranked timestamps.
By Clark Peng, Tolga Din\c{c}er
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding introduces a model‑agnostic framework that adaptively aligns EEG signals with visual semantics. It replaces fixed visual or textual anchors with EEG‑aware class‑level contrastive supervision and employs structure‑consistent interpolation to preserve channel‑wise and temporal importance. Across multiple evaluation settings—including subject‑dependent, subject‑independent, strict cross‑subject transfer, and continual adaptation—ProCA delivers significant performance gains, achieving relative Top‑1 improvements ranging from 7.4% to 28.1%.
By Kanglei Zhou, Chunyan Lan, Dongyang Li, Jun Zhu, Liyuan Wang
NeurDuo-EEG is a causal EEG foundation model that introduces channel‑resolved persistent memory and multi‑timescale memory management, allowing it to model continuous EEG with a fixed‑size state. Trained on 3,955 hours of EEG from 17 public datasets, it outperforms baselines on most short‑window and long‑sequence tasks, notably improving seizure detection AUC‑PR from 0.285 to 0.471. The Small variant achieves these gains with only 4.7 M parameters and supports efficient streaming inference with constant per‑chunk latency even as history grows to one hour.
By Yifan Wang, Haiping Liu, Yang Cui, Wenhao Cai, Shuhang Li, Xiaoyang Huang, Xianyang Liu, Jingyu Sun, Yizheng Sun, Cunhang Fan, Tianming Du, Jiancheng Yang, Zhenhong Li, Yunhao Zhang, Hongpeng Zhou, Jingyuan Sun
arXiv:2608.29304v1 Announce Type: new
Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...
By Xiangyu Liu, Zeting Yan, Zhitong Yin, Boyang Li, Xi Zhang
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
arXiv:2605.18251v2 Announce Type: replace-cross
Abstract: Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal...
By Yuwen Zeng, Dengzhe Hou, Zhang Zhang, Sai Sun, Yongsong Huang, Chia-huei Tseng, Satoshi Shioiri
The paper explores whether rapid, salience-driven behavior can be detected in high‑frequency Counter‑Strike 2 telemetry by reconstructing players’ perceptual states with a millisecond‑precision visibility engine. Using a 60–120 ms reaction window, the authors derive fight‑or‑flight labels and train two telemetry‑native models—EMBER (gradient boosting) and ORBIT (custom transformer)—which outperform a majority‑class baseline and a large language model while requiring far less inference time and cost. Adding a short post‑trigger window further improves accuracy to 85.15%, suggesting a potential probe of the shift from rapid to deliberative control, though the study remains hypothesis‑generating rather than mechanistic.
By Emanuel Dopater, Daniel Gavlas, Luk\'a\v{s} Jochymek, Kannan Paul, Miloslav Szczypka, David Voj\'a\v{c}ek, Josef \v{Z}\'a\v{c}ek, Jan Plucar, Radek Svoboda, Ivan Zelinka