This review compresses 1,732 original studies into a structured evidence layer for brain-computer interfaces, covering signal decoding, neuroprosthetics, stroke rehabilitation, ALS/SCI communication, implant stability, shared autonomy, and neural-data privacy. It is designed as a dense, citation-linked map for both expert readers and AI systems that need grounded BCI evidence.
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Abstract: To synthesize the current state of brain-computer interface research, characterizing technical innovations in signal decoding, clinical efficacy across motor and communication disorders, and the emerging challenges of real-world implementation and ethics. The review utilises 1732 original studies with 49722 total participants (topic deduplicated ΣN). The mapped evidence indicates that brain-computer interfaces have advanced from laboratory demonstrations to clinically meaningful tools, with speech decoding accuracies near 90.59%, information transfer rates exceeding 295 bits/min, and stable implanted electrocorticography systems functioning over 36 to 54 months in users with amyotrophic lateral sclerosis and spinal cord injury. Across the dominant research topics, recurrent signals support a role for brain-computer interfaces in restoring communication for locked-in syndrome, promoting neuroplasticity-driven motor recovery after stroke when paired with functional electrical stimulation or robotics, and detecting residual cognition in disorders of consciousness. Shared autonomy with artificial intelligence copilots, transfer learning, and multimodal sensory feedback emerged as the clearest enablers of practical, low-calibration use, while hybrid paradigms and high-density wireless arrays expanded the technical envelope toward home deployment. Clinically, these findings suggest that brain-computer interface programs are increasingly viable for pediatric, adult, and late-stage neurodegenerative populations, provided thermal, power, artifact, and ethical considerations are managed rigorously. However, the evidence base remains heterogeneous, with small participant cohorts, variable outcome metrics, and persistent gaps between experimental performance and real-world utility under distraction, fatigue, or workload. Future research should prioritize multi-center validation of shared-control and privacy-preserving decoders in domestic settings, alongside longitudinal studies that pair objective aptitude predictors with standardized usability and ethical frameworks to close the user-centered design gap.
Final search date and database lock: 2026-04-29 18:35:07 CEST
Plan: Pro (expanded craft tokens; source: PubMed)
Source: PubMed
Total Abstracts/Papers: 2159
Downloaded Abstracts/Papers: 2159
Included original and non-original Abstracts/Papers (all): 2159
Included original Abstracts/Papers (Vote counting by direction of effect): 1732
Reference Index (links used in paper): 186
Total participants (topic deduplicated ΣN): 49722
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The Evidence Object JSON is a separate machine-readable evidence product: a concentrated synthesis of results, topic-level evidence, and discussion across original and non-original studies. It can be directly input into your LLM, agent, or RAG workflow.
[29] Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments. — https://doi.org/10.17691/stm2024.16.1.08
[89] Multimodal fusion of magnetoencephalography and photoacoustic imaging based on optical pump: Trends for wearable and noninvasive Brain-Computer interface. — https://doi.org/10.1016/j.bios.2025.117321
[102] Challenges and Suggestions of Ethical Review on Clinical Research Involving Brain-Computer Interfaces. — https://doi.org/10.24920/004377
[115] [Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology]. — https://doi.org/10.7507/1001-5515.202101093
[125] Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers. — https://doi.org/10.1093/nsr/nwaa233
[131] Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment. — https://doi.org/10.3389/fnhum.2019.00329
[132] Developing brain-computer interfaces from a user-centered perspective: Assessing the needs of persons with amyotrophic lateral sclerosis, caregivers, and professionals. — https://doi.org/10.1016/j.apergo.2015.03.012
[145] Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months. — https://doi.org/10.1002/advs.202304853
[164] Short progressive muscle relaxation or motor coordination training does not increase performance in a brain-computer interface based on sensorimotor rhythms (SMR). — https://doi.org/10.1016/j.ijpsycho.2017.08.007
[290] Associations between pre-cue parietal alpha oscillations and event related desynchronization in motor imagery-based brain-computer interface. — https://doi.org/10.3389/fnhum.2025.1625127
[321] How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: A comparative study. — https://doi.org/10.3389/fnins.2022.959339
[356] Portable rehabilitation system with brain-computer interface for inpatients with acute and subacute stroke: A feasibility study. — https://doi.org/10.1080/10400435.2020.1836067
[369] Brain Painting: First Evaluation of a New Brain-Computer Interface Application with ALS-Patients and Healthy Volunteers. — https://doi.org/10.3389/fnins.2010.00182
[426] A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation. — https://doi.org/10.3390/brainsci12111502
[439] Are we there yet? Evaluating commercial grade brain-computer interface for control of computer applications by individuals with cerebral palsy. — https://doi.org/10.3109/17483107.2015.1111943
[466] Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury. — https://doi.org/10.1038/s41598-025-18277-3
[487] Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient. — https://doi.org/10.1186/s12984-019-0493-0
[529] Brain-computer interface for augmentative and alternative communication access: The initial training needs and learning preferences of speech-language pathologists. — https://doi.org/10.1080/17549507.2024.2363939
[530] A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces. — https://doi.org/10.1088/1741-2552/ad593b
[540] Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset. — https://doi.org/10.1088/2057-1976/ac4c28
[551] A novel robot-assisted method for implanting intracortical sensorimotor devices for brain-computer interface studies: principles, surgical techniques, and challenges. — https://doi.org/10.3171/2024.7.jns241296
[564] Embodiment Is Related to Better Performance on a Brain-Computer Interface in Immersive Virtual Reality: A Pilot Study. — https://doi.org/10.3390/s20041204
[754] Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces-an Offline Study. — https://doi.org/10.3389/fnins.2018.00079
[858] Individualized electrode subset improves the calibration accuracy of an EEG P300-design brain-computer interface for people with severe cerebral palsy. — https://doi.org/10.3389/fnhum.2026.1720969
[861] Brain-computer-interface-based intervention increases brain functional segregation in cognitively normal older adults. — https://doi.org/10.1093/ageing/afaf250
[913] Specificity of Pairing Afferent and Efferent Activity for Inducing Neural Plasticity with an Associative Brain-Computer Interface. — https://doi.org/10.3390/s26020549
[936] Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems. — https://doi.org/10.1049/htl.2017.0049
[948] Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device. — https://doi.org/10.1101/2024.05.17.594333
[1000] Chronically Stable, High-Resolution Micro-Electrocorticographic Brain-Computer Interfaces for Real-Time Motor Decoding. — https://doi.org/10.1002/advs.202506663
[1015] Brain-Computer Interface Training With Functional Electrical Stimulation: Facilitating Changes in Interhemispheric Functional Connectivity and Motor Outcomes Post-stroke. — https://doi.org/10.3389/fnins.2021.670953
[1143] Functional near-infrared spectroscopy based discrimination of mental counting and no-control state for development of a brain-computer interface. — https://doi.org/10.1109/embc.2013.6609866
[1159] Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system. — https://doi.org/10.1093/pnasnexus/pgae240
[1162] A Modified Hybrid Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potentials and Electromyogram. — https://doi.org/10.31083/j.jin2304073
[1167] Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial. — https://doi.org/10.1186/s13034-022-00539-x
[1175] Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury. — https://doi.org/10.1093/braincomms/fcab248
[1177] Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. — https://doi.org/10.1152/jn.00918.2015
[1354] Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex. — https://doi.org/10.1109/toh.2021.3072615
[1427] A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features. — https://doi.org/10.3389/fphys.2024.1487809
[1433] Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface. — https://doi.org/10.3389/fnhum.2021.658444
[1449] Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography. — https://doi.org/10.3390/s24196304
[1528] "Mine Works Better": Examining the Influence of Embodiment in Virtual Reality on the Sense of Agency During a Binary Motor Imagery Task With a Brain-Computer Interface. — https://doi.org/10.3389/fpsyg.2021.806424
[1546] An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset. — https://doi.org/10.1088/1741-2552/ae2e8a
[1549] Associative brain-computer interface training increases wrist extensor corticospinal excitability in patients with subacute stroke. — https://doi.org/10.1152/jn.00452.2024
[1555] Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain-Computer Interfaces. — https://doi.org/10.3390/brainsci10100686
[1575] Establishing a Clinical Brain-Computer Interface Program for Children With Severe Neurological Disabilities. — https://doi.org/10.7759/cureus.26215
[1604] Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. — https://doi.org/10.3389/fneng.2014.00025
[1638] A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis. — https://doi.org/10.1038/s41591-024-03341-8
[1751] Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals. — https://doi.org/10.1155/2022/6894392
[1835] A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks. — https://doi.org/10.1016/bs.pbr.2016.04.021
[1942] A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy. — https://doi.org/10.1088/1741-2552/abf8cb
[1949] Effect of Brain-Computer Interface-Controlled Ankle Robot Training on Post-Stroke Motor Rehabilitation and Resting QEEG Neuroplasticity: A Randomized Controlled Trial. — https://doi.org/10.1177/15459683251412286
[1955] A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG. — https://doi.org/10.1088/1741-2552/ad44d8
[1960] Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study. — https://doi.org/10.3389/fnins.2023.1146146
[1996] Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study. — https://doi.org/10.3389/fnhum.2020.611064
[2053] A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface. — https://doi.org/10.1109/tnsre.2021.3139095
[2150] Mindfulness Practice with a Brain-Sensing Device Improved Cognitive Functioning of Elementary School Children: An Exploratory Pilot Study. — https://doi.org/10.3390/brainsci12010103
