This scoping review examines how brain‑computer interfaces (BCIs) can restore sensory and motor functions in people with severe neurological impairment. It introduces a unified 2×2 framework (invasiveness × signal direction) to map 31 key studies, highlighting that most evidence comes from invasive, efferent‑restoration approaches post‑2015. The paper outlines a roadmap toward closed‑loop, bidirectional restoration and identifies gaps in metric standardization, longitudinal data, and cross‑community collaboration.
arXiv:2606. 15091v1 Announce Type: cross Abstract: Millions of individuals worldwide suffer from sensory and communication deficits caused by neurodegenerative diseases, stroke, or trauma.
arXiv:2609.36736v1 Announce Type: cross Abstract: Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative in...
arXiv:2609.27650v4 Announce Type: replace Abstract: Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expres...
The article surveys brain‑to‑language decoding, covering tasks, neural signals, methods, and evaluation across invasive and non‑invasive modalities. It traces the field’s evolution from constrained recognition to text generation, streaming speech, and facial animation, linking tasks to neural populations and decoder representations. The review highlights complementary decoding targets, shared representations, and the growing importance of calibration, feedback, and user control for online communication, while proposing a five‑level future trajectory toward bidirectional cognitive exchange.
arXiv:2607. 05165v1 Announce Type: new Abstract: Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurgery.
arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.
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:2608. 13576v1 Announce Type: cross Abstract: Brain-computer interface (BCI) research relies on multistage computational pipelines, yet progress remains constrained by fragmented data formats, heterogeneous decoder implementations and hardware-specific deployment toolchains, and researchers lack an integrated workflow.
arXiv:2606. 08594v1 Announce Type: new Abstract: Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility.
The paper introduces Open‑Vocabulary Mutual Information (OVMI), an information‑theoretic metric that quantifies how much of a user’s intended speech a speech brain‑computer interface (BCI) can convey relative to a reference word distribution. OVMI enables comparison of systems that use different vocabularies, recording methods, and datasets, revealing that conventional metrics like accuracy and word error rate can overstate performance. Using OVMI, the authors compare existing speech BCI systems, expose trade‑offs between vocabulary coverage and decoding accuracy, and show that optimizing vocabulary selection for OVMI can improve accuracy by up to 16.3% across three speech domains.
The study investigates whether Low‑Rank Adaptation (LoRA) can adapt three pretrained EEG foundation models—LaBraM‑base, REVE‑base, and REVE‑large—for binary left‑ vs. right‑hand motor imagery decoding in stroke patients. Using subject‑wise five‑fold cross‑validation on the PhysioNet EEG Motor Movement/Imagery Dataset and a binary subset of the UET175 stroke dataset, LoRA significantly improved accuracy for LaBraM‑base (0.822) and REVE‑base (0.957) on the healthy cohort, but only REVE‑base LoRA achieved high performance (0.847±0.194) on stroke data, with a best mean accuracy of 0.952 in leave‑one‑subject‑out evaluation. The results demonstrate that healthy‑benchmark performance does not guarantee transfer to stroke EEG, highlighting the need for target‑domain adaptation and subject‑level assessment in rehabilitation BCIs.
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.