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.
arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
arXiv:2605. 29263v3 Announce Type: replace Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased sampling of the scalp potential field.
arXiv:2605. 29263v2 Announce Type: replace Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased view of distributed scalp activity.
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.
iMINDBench is a new benchmark for intracranial electroencephalography (iEEG) neural decoding that evaluates models on fifteen tasks across three naturalistic movie‑watching datasets from multiple institutions. It standardizes preprocessing tracks and evaluation splits to enable consistent comparisons. The study shows that pretrained systems outperform baselines within their tracks, but strong spectral baselines remain competitive, and scaling up supervised data yields only modest or task‑dependent gains.
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...
arXiv:2608.24597v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised...
arXiv:2608.24727v1 Announce Type: cross Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...
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.
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.
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:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.