The study introduces a meta-learning and pretraining approach to improve neural stimulation response modeling. By extending temporal basis function models with a MAML-based architecture, the authors demonstrate a significant reduction in catastrophic forecast failures and narrower prediction intervals across 40 optogenetic stimulation sessions in non-human primates. The method also cuts calibration requirements by 50–90%, making closed‑loop stimulation more feasible within clinical time constraints.
By Matthew J Bryan, Daniel C Muir, Felix Schwock, Azadeh Yazdan-Shahmorad, Rajesh P N Rao
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:2608.24727v1 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
arXiv:2606. 00074v1 Announce Type: cross Abstract: Reliable seizure prediction is a prerequisite for closed-loop neurostimulation therapy, yet existing methods rarely account for the variability in EEG signal quality encountered in real-world deployment, and the overwhelming majority adopt non-strict evaluation protocols that overestimate generalisation performance.
By Mufeng Chen, Qi Wu, Bingchao Huang, Xiwen Lai, Zekai Chen, Xinge Ouyang, Quansheng Ren
arXiv:2609.27729v1 Announce Type: cross
Abstract: In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or...
By Marco Rothermel, Madleen Stenger, Soroush Daftarian, Svenja Jule Francke, Bita Shariatpanahi, Jos\'e C. Garc\'ia Alanis, Mohammad-Ali Nikouei Mahani, Stefan G. Hofmann, Tim Hahn, Hamidreza Jamalabadi
The paper demonstrates that meta‑learning and pretraining can improve neural stimulation response models, reducing catastrophic forecast failures and narrowing prediction intervals. Using temporal basis function models with a MAML‑based architecture, the authors evaluated 40 optogenetic stimulation sessions in non‑human primates and found that a 1,000‑sample calibration set reduced poor‑performance sessions from 16 to 1. Calibration needs were cut by 50–90%, making closed‑loop stimulation feasible within clinical time limits.
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tun...
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
arXiv:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.
By Marzieh Zare
arXiv:2607. 11578v1 Announce Type: cross Abstract: Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\% of clinical recordings.
By Abdulkader Helwan, Lina Abou-Abbas, Hussein El Amouri, Belkacem Chikhaoui, Khadidja Henni
The article reviews the emerging field of virtual intracranial EEG (iEEG) derived from scalp EEG recordings. It introduces a target‑centred framework that separates event inference, feature translation, and waveform reconstruction, and it evaluates evidence based on cohort independence, coverage, and validation rigor. Current research shows limited success in inferring specific intracranial events and low‑frequency activity, but it has not yet achieved reliable reconstruction of arbitrary contact‑level signals.
By Dongyi He, Xiangkai Wang, Hongjie Yan, Luping Song, Wai Ting Siok, Nizhuan Wang
arXiv:2607. 25907v1 Announce Type: cross Abstract: Activation steering controls model behavior by editing internal activations at inference time.
By Deepanshu Mody, Samarth Agarwal, Utkarsh Mittal, Dipesh Mahato