arXiv AI August 20, 2026 By Xuan-The Tran

Sensory Restoration via Brain-Computer Interfaces: A Scoping Review

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 16

Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap

arXiv:2606. 15091v1 Announce Type: cross Abstract: Millions of individuals worldwide suffer from sensory and communication deficits caused by neurodegenerative diseases, stroke, or trauma.

By Xuan-The Tran

arXiv Machine Learning

Jul 7

Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech

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.

By Benjamin Ballyk, Teyun Kwon, Miran \"Ozdogan, Oiwi Parker Jones

multimodal

arXiv Machine Learning

Aug 7

MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis

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.

By Yuanhao Chen, Peter Chin

llms rag benchmarks safety

arXiv Machine Learning

Aug 17

BCIJelly: An integrated ecosystem for brain-computer interface research

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.

By Liyuan Han, Xinrui Yang, Tianyu Zheng, Qizhi Yang, Yitao Qin, Liang Chen, Qinglai Wei, Binjie Hong, Xinhe Zhang, Rui Xiong, Yong Gu, Mu-ming Poo, Bo Xu, Chengyu Li, Tielin Zhang

llms benchmarks

arXiv Machine Learning

Jun 9

How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap

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.

By Jasmeet Singh Bindra, Siddharth Panwar, Shubhajit Roy Chowdhury

llms efficiency benchmarks

arXiv Machine Learning

Aug 6

STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding

arXiv:2608. 02070v2 Announce Type: replace-cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.

By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu

safety

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Related stories

Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap

Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech

MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis

BCIJelly: An integrated ecosystem for brain-computer interface research

How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap

STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding