arXiv:2607. 20720v1 Announce Type: cross Abstract: Harnessing the potential of electroencephalography (EEG) for brain research is fundamentally limited by intrinsic noise and the diffuse projection of brain-generated activity over the scalp.
arXiv:2608. 20186v1 Announce Type: new Abstract: Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable.
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.
NeuroDETR is a novel multiscale discrete spatiotemporal tokenization framework designed for asynchronous EEG event detection. It combines structured tokenization, event-aware feature refinement, and a sparse attention module called FOCAL to capture both brief and sustained neural responses while preserving spatial hierarchy across EEG channels. Experiments on eight diverse EEG datasets show that NeuroDETR achieves state‑of‑the‑art performance in jointly classifying events and estimating their onsets and offsets.
The study investigates how information about words and passage context is represented across the layers of the Llama‑3.1‑8B language model during silent reading, using EEG recordings. Decoders trained on each layer show that word‑level decodability peaks in early layers and diminishes toward the final layer, while a context‑tracking component increases with depth. Even a single 0.5‑second EEG window can capture passage‑level information, and control experiments confirm that this context tracking is driven by the text rather than by word decoding or session artifacts.
The paper introduces PAIR, an EEG benchmark that records brain activity from 20 participants while they watch naturalistic video clips and immediately recall them. The dataset allows direct comparison between perception and recall of the same content, and the authors evaluate several decoding models on tasks ranging from fine‑grained semantic classification to meta‑feature decoding (motion, color, object number, face, human presence) and a state‑decoding task distinguishing perception from recall. Results show comparable performance across many model–task pairs, with slightly higher perception accuracy in some coarse‑grained comparisons, and strong separability between perception and recall at the state level, even after removing alpha‑band activity.
arXiv:2601. 01095v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in videos remains underexplored.
arXiv:2608.28699v1 Announce Type: new Abstract: Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine...
arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
The paper introduces BraVista, a visual-language framework that transforms multichannel EEG signals into structured images and uses instruction‑conditioned vision‑language models for unified multi‑task EEG decoding. By continuing post‑training of a general‑domain VLM, BraVista adapts to neural signals without large‑scale EEG‑specific pretraining, achieving strong performance across sleep staging, emotion recognition, cognitive workload classification, and abnormal EEG detection. Analyses show that the EEG‑to‑image representation is crucial and that the model’s performance degrades gradually with added noise, indicating reliance on genuine EEG information.
EEG-to-Report is a browser-based annotation and feature‑text framework that links routine EEG review with the creation of AI‑ready datasets. It ingests multi‑format EEG data, standardizes channels, and provides an interactive viewer with a multimodal annotation layer that combines typed text and transcribed voice notes. For each annotated segment, a feature extraction engine computes standardized spectral, temporal, entropy, Hjorth, connectivity, and spike‑related descriptors, stored alongside clinical descriptions in a portable JSON schema, producing aligned feature‑text pairs for training multimodal EEG‑language models. The framework also includes an auto‑report module that uses an ensemble of convolutional networks and a large language model to draft clinical narratives for neurologist review, thereby streamlining annotation workflows and enabling editable draft reports.
The paper presents a reproducible single‑subject baseline for reconstructing visual stimuli from EEG using a temporal‑spatial convolutional encoder that maps averaged EEG signals to 512‑dimensional ViT-B/32 image features. On the THINGS‑EEG2 dataset, the model achieves 12.83%, 39.17%, and 58.00% image recall at ranks 1, 5, and 10, respectively, outperforming analytical chance levels. The study also shows that performance drops sharply when applying a model trained on one subject to others, and that direct conditional generators without external visual weights produce noise‑dominated outputs, indicating that only coarse semantic decoding is feasible under the tested protocol.
arXiv:2608.29304v1 Announce Type: new Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...