A near-, medium-, and long-term roadmap toward closed-loop, bidirectional restoration is outlined, and gaps in metric standardization, longitudinal evidence, and cross-community collaboration are identified as priorities for future research.
Abstract
Brain-computer interfaces (BCIs) can restore sensory and motor function in individuals with severe neurological impairment, but the literature is fragmented between invasive neuroprosthetics and non-invasive electrophysiological decoders, with inconsistent terminology and metrics. This scoping review maps BCI-mediated sensory restoration along a unified 2x2 framework (invasiveness x signal direction), charts representative modalities and their trade-offs, and synthesizes a convergence roadmap for the field. Eligible sources were peer-reviewed studies, clinical trials, and authoritative reviews on BCI or neuroprosthetic systems for sensory or motor restoration, substitution, or augmentation, published in English between 1969 and 2025, restricted to high-impact venues to prioritize landmark evidence. Rather than an exhaustive database search, we charted a purposively assembled, citation-chained corpus of 31 pivotal sources for modality, signal type, invasiveness, signal direction, resolution, clinical risk, cost, and regulatory maturity. We define and distinguish restoration, substitution, and augmentation, and map the corpus onto the four quadrants of the framework. The corpus is dominated by efferent restoration (21 of 31) and invasive interfaces (22 of 31), and is concentrated after 2015 (25 of 31). Non-invasive, AI-augmented silent-speech decoding has matured rapidly since 2023, while invasive speech and motor neuroprostheses have achieved near-conversational communication rates. The unified taxonomy clarifies trade-offs between pathways and the role of foundation models in closing the gap between them. We outline a near-, medium-, and long-term roadmap toward closed-loop, bidirectional restoration, and identify gaps in metric standardization, longitudinal evidence, and cross-community collaboration as priorities for future research.
Brain–computer interfaces (BCIs) are opening new avenues for treating neurological disorders and physical impairments, yet clinical practice continues to be split between invasive and non-invasive strategies. This work approaches BCI medical applications from three angles: a systematic review of the principal literature, a comparative case analysis contrasting invasive techniques (electrocorticography, microelectrode arrays) with non-invasive ones (electroencephalography, functional near-infrared spectroscopy), and an interdisciplinary assessment that draws together neuroscience, engineering, and ethical perspectives. The findings indicate that invasive BCIs offer strong motor and sensory restoration—for instance, robotic arm control reaching 80–100% task success rates and partial recovery of hand movement—but are limited by surgical hazards, progressive signal deterioration, and price tags above $250,000. Non-invasive BCIs are safer and have seen wider deployment in community-based neurorehabilitation (e.g., around 70% effectiveness for post-stroke upper-limb recovery); nevertheless, they struggle with inherently poor signal-to-noise ratios, a BCI illiteracy rate near 30%, and low information transfer speeds. Current trends spotlight the domestication of non-invasive systems, multimodal sensor fusion, adaptive algorithms, and attempts to clear clinical translation hurdles. Importantly, the assessment reveals mounting ethical and societal strains—neural data privacy, autonomy paradoxes, inequitable access, stigmatization, and military coercion among them. The study stresses the necessity of embedding relational autonomy and neural rights into BCI development, tying technological trajectories to governance demands in order to shape responsible paths for future neurotechnologies.
Yuzhang Wu· Theoretical and Natural Scie...· 0 citations
Paraplegia, most commonly caused by spinal cord injury (SCI), results in motor, sensory, and autonomic dysfunction below the level of injury. Traditional rehabilitation primarily relies on passive training, which has limited effects on central neural remod eling. As a cutting-edge neuromodulation technology, brain-computer interfaces (BCIs) can bypass the damaged spinal cord and directly translate brain signals into commands for external devices, offering a new pathway for functional reconstruction. Focusing on the neurorehabilitation and nursing perspectives of patients with paraplegia, this review systematically summarizes the definition, principles, classification, and clinical value of non -invasive EEG‑BCI and invasive implantable BCI. It outlines stratif ied rehabilitation strategies for patients with different injury severities and constructs a comprehensive nursing model that includes pre -rehabilitation assessment, intra -training monitoring, complication prevention, psychological intervention, and home -based continuing care. Current evidence indicates that BCIs can effectively activate neuroplasticity, relieve spasticity, and improve motor intention and activities of daily living. Professional nursing is critical for ensuring safety, adherence, and long-term outcomes. This review also discusses current research limitations and proposes future directions to inform clinical practice and academic research.
The steady-state visual evoked potential (SSVEP), the brain's oscillatory response to repetitive visual stimulation (RVS), has emerged as a powerful tool in neuroscience with wide-ranging applications in multiple disciplines. This review provides a scoping, narrative roadmap of SSVEP applications organized into three primary domains: fundamental research in vision and cognition, clinical neuroscience, and neural engineering. Although these fields differ in focus, they often converge in their use of similar research questions, stimulation paradigms, analysis techniques, and application scenarios. At the same time, specialization may have created knowledge silos that limit cross-disciplinary transfer of methods and insights. By bridging findings from seemingly disparate domains, this review highlights the versatility of SSVEPs in investigating neural mechanisms, supporting diagnosis and treatment of neurological and psychiatric conditions, and advancing brain-computer interface technology. We conclude with cross-field insights on how stimulus and analysis choices affect interpretation and usability, and we outline directions for improving the comparability and transferability of SSVEP research and applications.
T. Tsoneva, P. Desain, G. G. Molina et al.· NeuroImage· 0 citations
Restoration of dynamic motor function following neurological injury increasingly relies on adaptive neuroprostheses, which require real-time sensory feedback to continuously adjust to a user’s physical state. However, it remains unknown whether distinct afferent activity can even be decoded from highly overlapping, volume-conducted epidural fields. This challenge is particularly pronounced in the lumbosacral enlargement, where common fibular (CFN) and tibial nerve (TN) afferents converge extensively, producing highly similar cord dorsum potential (CDP) topographies. Here, we demonstrate for the first time in humans that clinical-grade lumbosacral epidural paddle arrays capture sufficient fine-scale spatiotemporal structure to decode these overlapping inputs. Using a 32-contact array and peripheral nerve stimulation, we constructed a 42-dimensional feature space capturing distributed amplitudes, field geometry, and waveform morphology. A support vector machine decoded four distinct afferent classes (left and right CFN and TN) with a median accuracy of 90.9% ± 0.3%. Shapley Additive Explanations revealed decoding was driven by contact-level voltage patterns and temporal waveform complexity, while the geometric features contributed minimally. These afferent-specific signatures persisted even at sub-motor threshold stimulation intensities. By utilizing standard clinical arrays, this approach provides a pathway toward rapid deployment of interpretable closed-loop neuromodulation, avoiding the surgical risks of penetrating or peripheral interfaces.
Alexander G. Steele, M. Candela, Gracie Hufft et al.· Research Square· 0 citations
Brain-computer interfaces (BCIs) have emerged as one of the most significant technological innovations in modern neurosurgery, integrating neuroscience, biomedical engineering, artificial intelligence, and clinical neurology to establish direct communication between the brain and external devices. This review provides a comprehensive analysis of the current scientific evidence regarding the neurophysiological foundations, technological evolution, clinical applications, limitations, and future perspectives of BCIs in neurosurgical practice. A qualitative documentary review was conducted following the Scientific Method, using peer-reviewed literature retrieved from internationally recognized databases, including PubMed, Scopus, Web of Science, ScienceDirect, IEEE Xplore, and Google Scholar. The analyzed evidence indicates that current clinical applications are primarily focused on motor restoration, neurorehabilitation, communication recovery, speech neuroprostheses, and adaptive neuromodulation for neurological disorders such as spinal cord injury, stroke, Parkinson's disease, amyotrophic lateral sclerosis, and drug-resistant epilepsy. Recent advances in machine learning, implantable electrodes, wireless technologies, and closed-loop therapeutic systems have substantially improved neural decoding performance and expanded the potential clinical utility of BCIs. However, important challenges remain regarding long-term signal stability, biocompatibility, surgical safety, economic accessibility, regulatory frameworks, and equitable implementation across different healthcare systems. The available evidence suggests that continued multidisciplinary collaboration and technological innovation will facilitate the progressive integration of BCIs into routine neurosurgical care, supporting personalized therapeutic strategies aimed at improving functional recovery, communication, and quality of life in patients with complex neurological disorders.
Alejandra Mendoza Ortiz, Marco Antonio, Ortiz Ayala et al.· International science journa...· 0 citations
Lower limb motor dysfunction resulting from neurological injuries presents a significant clinical and engineering challenge. Brain-Computer Interface (BCI) technology offers a direct neural pathway for controlling assistive devices, yet the comparative efficacy of different BCI paradigms remains insufficiently quantified. This study presents a systematic engineering evaluation of three primary BCI paradigms-P300, Steady-State Visual Evoked Potential (SSVEP), and Motor Imagery (MI)-applied to lower limb rehabilitation. We analyze their performance across four quantitative dimensions: walking ability, physiological function, motor control, and quality of life. Clinical data analysis reveals that MI-based systems combined with physical training yield the most significant improvements in muscle strength (e.g., hip flexor strength increased from 2.58±0.44 kg to 3.46±0.66 kg over 4 weeks, p<0.001). P300 paradigms demonstrate high stability for long-term function maintenance, evidenced by significant amplitude increases (from $6.16 \pm 3.34 \mu \mathrm{V}$ to $9.52 \pm 2.66 \mu \mathrm{V}, \mathrm{p}=0.001$) correlating with neural recovery. SSVEP systems excel in high-precision gait training due to their robust frequency response. Furthermore, hybrid paradigms (e.g., MI-SSVEP) show superior potential for enhancing neural plasticity. This comparative analysis provides a technical framework for selecting and optimizing BCI paradigms based on specific rehabilitation engineering requirements.
Jie Zhang, Jiahe Zhang, Jiahao Lu· 2026 International Conferenc...· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
MIT News · Artificial Intelligence· news.mit.eduJul 7, 2026
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
