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.
By Xuan-The Tran
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
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
arXiv:2603. 19100v2 Announce Type: replace Abstract: Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity.
By Dana\'e Broustail, Anna Tegon, Thorir Mar Ingolfsson, Yawei Li, Luca Benini
arXiv:2608. 02070v1 Announce Type: 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
arXiv:2607. 22733v1 Announce Type: cross Abstract: We investigate whether a generative model can supply useful synthetic motor-imagery (MI) electroencephalography (EEG) trials that improve the accuracy of independent downstream classifiers.
By Matei Moldoveanu, Alain Sirois, Claire Ben Ali, Fabien Lotte, Florian Yger
arXiv:2510. 15371v2 Announce Type: replace-cross Abstract: Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation support for patients with motor impairments.
By Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura
arXiv:2606. 01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability.
By Yangxuan Zhou, Sha Zhao, Jiquan Wang, Shijian Li, Gang Pan
arXiv:2607. 24126v1 Announce Type: cross Abstract: Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invasive electroencephalography (EEG).
By Sankalp Sunil Turankar, Yogesh Kumar Meena
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...
By Yulong Dou, Han Wu, Guo Chen, Fangmao Ju, Zhiming Cui, Dinggang Shen
arXiv:2607. 03925v1 Announce Type: new Abstract: EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks.
By Weibin Li, Wendu Li, Yushan You, Chen Wei, Quanying Liu
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