Brain-computer interfaces: Transforming healthcare and navigating ethical boundaries

Yuan-Yuan Li , Maxwell M. Gilchrist , Franklin R. Tay , Yan Jin

Dental Research ›› 2026, Vol. 1 ›› Issue (3) : 100040

PDF (6309KB)
Dental Research ›› 2026, Vol. 1 ›› Issue (3) :100040 DOI: 10.1016/j.dtrs.2026.100040
Review Article
research-article
Brain-computer interfaces: Transforming healthcare and navigating ethical boundaries
Author information +
History +
PDF (6309KB)

Abstract

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. They hold promise for treating neurological disorders, restoring motor function, and enhancing human-computer interaction. Although non-invasive BCIs dominate research due to their accessibility and safety, advances in invasive and minimally-invasive technologies have improved signal fidelity and expanded applications. Despite these advancements, ethical concerns such as data privacy, informed consent, mental autonomy, and potential misuse must be addressed to ensure responsible implementation. Regulatory frameworks have been struggling to keep pace with rapid technological progress, raising concerns about safety, standardization, and long-term viability. Practical barriers such as high costs, limited accessibility, and the need for specialized training further complicate deployment. Additionally, the long-term neural impact of invasive BCIs and their seamless integration into daily life remain areas of active investigation. This review provides a comprehensive overview of the current BCI landscape. It examines foundational biosignals and technological approaches, highlights groundbreaking applications in healthcare (including neuroprosthetics, rehabilitation, and cognitive enhancement), and offers a critical analysis of the ethical, regulatory, and technical challenges. Ultimately, the review reflects the immense transformative potential and the multifaceted complexities of BCIs, emphasizing the imperative for responsible innovation and robust interdisciplinary collaboration to navigate their future development.

Keywords

Brain-computer interface / Ethics / Regulation / Personalized medical treatment

Cite this article

Download citation ▾
Yuan-Yuan Li, Maxwell M. Gilchrist, Franklin R. Tay, Yan Jin. Brain-computer interfaces: Transforming healthcare and navigating ethical boundaries. Dental Research, 2026, 1 (3) : 100040 DOI:10.1016/j.dtrs.2026.100040

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

X. Gao, Y. Wang, X. Chen, et al., Interface, interaction, and intelligence in generalized brain-computer interfaces, Trends Cogn. Sci. 25 (2021) 671-684, https://doi.org/10.1016/j.tics.2021.04.003.

[2]

C.L. Hughes, S.N. Flesher, J.M. Weiss, et al., Perception of microstimulation frequency in human somatosensory cortex, Elife 10 (2021) e65128, https://doi.org/10.7554/eLife.65128.

[3]

M.S. Guellil, F. Kies, E.K. Hussein, et al., Pushing the boundaries of brain-computer interfacing (BCI) and neuron-electronics, J. Neurosci. Methods 411 (2024) 110274, https://doi.org/10.1016/j.jneumeth.2024.110274.

[4]

K.M. Patrick-Krueger, I. Burkhart, J.L. Contreras-Vidal, The state of clinical trials of implantable braincomputer interfaces, Nat. Rev. Bioeng. 3 (2025) 50-67, https://doi.org/10.1038/s44222-024-00239-5.

[5]

B.J. Edelman, S. Zhang, G. Schalk, et al., Non-invasive brain-computer interfaces: state of the art and trends, IEEE Rev. Biomed. Eng. 18 (2025) 26-49, https://doi.org/10.1109/RBME.2024.3449790.

[6]

J. Tang, A. LeBel, S. Jain, et al., Semantic reconstruction of continuous language from non-invasive brain recordings, Nat. Neurosci. 26 (2023) 858-866, https://doi.org/10.1038/s41593-023-01304-9.

[7]

H. Zhang, L. Jiao, S. Yang, et al., Brain-computer interfaces: the innovative key to unlocking neurological conditions, Int J. Surg. 110 (2024) 5745-5762, https://doi.org/10.1097/JS9.0000000000002022.

[8]

Y. Tang, D. Chen, H. Liu, et al., Deep EEG superresolution via correlating brain structural and functional connectivities, IEEE Trans. Cyber 53 (2023) 4410-4422, https://doi.org/10.1109/TCYB.2022.3178370.

[9]

Y. Liu, Z. Zhao, M. Xu, et al., Decoding and synthesizing tonal language speech from brain activity, Sci. Adv. 9 (2023) eadh0478, https://doi.org/10.1126/sciadv.adh0478.

[10]

F.R. Willett, D.T. Avansino, L.R. Hochberg, et al., High-performance brain-to-text communication via handwriting, Nature 593 (2021) 249-254, https://doi.org/10.1038/s41586-021-03506-2.

[11]

S.R. Nason, M.J. Mender, A.K. Vaskov, et al., Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface, e8, Neuron 109 (2021) 3164-3177, https://doi.org/10.1016/j.neuron.2021.08.009.

[12]

X. Si, H. He, J. Yu, et al., Cross-subject emotion recognition brain-computer interface based on fNIRS and DBJNet, Cyborg Bionic Syst 4 (2023) 0045, https://doi.org/10.34133/cbsystems.0045.

[13]

Y. Qiao, X. Li, J. Wang, et al., Intelligent and multifunctional graphene nanomesh electronic skin with high comfort, Small 18 (2022) e2104810, https://doi.org/10.1002/smll.202104810.

[14]

D. Kireev, S.K. Ameri, A. Nederveld, et al., Fabrication, characterization and applications of graphene electronic tattoos, Nat. Protoc. 16 (2021) 2395-2417, https://doi.org/10.1038/s41596-020-00489-8.

[15]

J. Kubicek, K. Fiedorova, D. Vilimek, et al., Recent trends, construction, and applications of smart textiles and clothing for monitoring of health activity: a comprehensive multidisciplinary review, IEEE Rev. Biomed. Eng. 15 (2022) 36-60, https://doi.org/10.1109/RBME.2020.3043623.

[16]

S.M. Won, L. Cai, P. Gutruf, et al., Wireless and battery-free technologies for neuroengineering, Nat. Biomed. Eng. 7 (2023) 405-423, https://doi.org/10.1038/s41551-021-00683-3.

[17]

J.P. Wright, I.T. Mughrabi, J. Wong, et al., A fully implantable wireless bidirectional neuromodulation system for mice, Biosens. Bioelectron. 200 (2022) 113886, https://doi.org/10.1016/j.bios.2021.113886.

[18]

B. Wang, C.M. Wong, Z. Kang, et al., Common spatial pattern reformulated for regularizations in brain-computer interfaces, IEEE Trans. Cyber 51 (2021) 5008-5020, https://doi.org/10.1109/TCYB.2020.2982901.

[19]

H. Yue, Z. Chen, W. Guo, et al., Research and application of deep learning-based sleep staging: data, modeling, validation, and clinical practice, Sleep Med. Rev. 74 (2024) 101897, https://doi.org/10.1016/j.smrv.2024.101897.

[20]

D. Kumar, H. Li, D.D. Kumbhar, et al., Highly efficient back-end-of-line compatible flexible sibased optical memristive crossbar array for edge neuromorphic physiological signal processing and bionic machine vision, Nanomicro Lett 16 (2024) 238, https://doi.org/10.1007/s40820-024-01456-8.

[21]

M. Sharifshazileh, K. Burelo, J. Sarnthein, et al., An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG, Nat. Commun. 12 (2021) 3095, https://doi.org/10.1038/s41467-021-23342-2.

[22]

L.L. Oganesian, M.M. Shanechi, Brain-computer interfaces for neuropsychiatric disorders, Nat. Rev. Bioeng. 2 (2024) 653-670, https://doi.org/10.1038/s44222-024-00177-2.

[23]

W. Wang, F. Qi, D.P. Wipf, et al., Sparse Bayesian learning for end-to-end EEG decoding, IEEE Trans. Pattern Anal. Mach. Intell. 45 (2023) 15632-15649, https://doi.org/10.1109/TPAMI.2023.3299568.

[24]

Y. Fan, H. Mao, Q. Li, A model-agnostic feature attribution approach to magnetoencephalography predictions based on Shapley value, IEEE J. Biomed. Health Inf. 27 (2023) 2524-2535, https://doi.org/10.1109/JBHI.2023.3248139.

[25]

S. Latheef, Brain to Brain Interfaces (BBIs) in future military operations; blurring the boundaries of individual responsibility, Monash Bioeth. Rev. 41 (2023) 49-66, https://doi.org/10.1007/s40592-022-00171-7.

[26]

M. Mahmood, S. Kwon, H. Kim, et al., Wireless soft scalp electronics and virtual reality system for motor imagery-based brain-machine interfaces, Adv. Sci. 8 (2021) e2101129, https://doi.org/10.1002/advs.202101129.

[27]

M. Mahmood, N. Kim, M. Mahmood, et al., VR-enabled portable brain-computer interfaces via wireless soft bioelectronics, Biosens. Bioelectron. 210 (2022) 114333, https://doi.org/10.1016/j.bios.2022.114333.

[28]

K. Shen, O. Chen, J.L. Edmunds, et al., Translational opportunities and challenges of invasive electrodes for neural interfaces, Nat. Biomed. Eng. 7 (2023) 424-442, https://doi.org/10.1038/s41551-023-01021-5.

[29]

S.P. Savya, F. Li, S. Lam, et al., In vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation, Biomaterials 289 (2022) 121784, https://doi.org/10.1016/j.biomaterials.2022.121784.

[30]

S. Luo, M. Angrick, C. Coogan, et al., Stable decoding from a speech BCI enables control for an individual with ALS without recalibration for 3 months, Adv. Sci. 10 (2023) e2304853, https://doi.org/10.1002/advs.202304853.

[31]

P. Mitchell, S.C.M. Lee, P.E. Yoo, et al., Assessment of safety of a fully implanted endovascular braincomputer interface for severe paralysis in 4 patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) study, JAMA Neurol 80 (2023) 270-278, https://doi.org/10.1001/jamaneurol.2022.4847.

[32]

A.L. Benabid, T. Costecalde, A. Eliseyev, et al., An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: A proof-of-concept demonstration, Lancet Neurol 18 (2019) 1112-1122, https://doi.org/10.1016/S1474-4422(19)30321-7.

[33]

W.S. Griggs, S.L. Norman, T. Deffieux, et al., Decoding motor plans using a closed-loop ultrasonic brain-machine interface, Nat. Neurosci. 27 (2024) 196-207, https://doi.org/10.1038/s41593-023-01500-7.

[34]

S.L. Norman, D. Maresca, V.N. Christopoulos, et al., Single-trial decoding of movement intentions using functional ultrasound neuroimaging, Neuron 109 (2021) 1554-1566.e4, https://doi.org/10.1016/j.neuron.2021.03.003.

[35]

T.J. Oxley, P.E. Yoo, G.S. Rind, et al., Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: First in-human experience, J. Neurointerv Surg. 13 (2021) 102-108, https://doi.org/10.1136/neurintsurg-2020-016862.

[36]

E. McGlynn, V. Nabaei, E. Ren, et al., The future of neuroscience: flexible and wireless implantable neural electronics, Adv. Sci. 8 (2021) 2002693, https://doi.org/10.1002/advs.202002693.

[37]

M. Vomero, F. Ciarpella, E. Zucchini, et al., On the longevity of flexible neural interfaces: establishing biostability of polyimide-based intracortical implants, Biomaterials 281 (2022) 121372, https://doi.org/10.1016/j.biomaterials.2022.121372.

[38]

K. Chen, S.M. Wellman, Y. Yaxiaer, et al., vivo spatiotemporal patterns of oligodendrocyte and myelin damage at the neural electrode interface, Biomaterials 268 (2021) 120526, https://doi.org/10.1016/j.biomaterials.2020.120526.

[39]

A. Chen, L. Xi, T. Li, et al., Highly sensitive low-frequency acoustic sensor based on functionalized graphene oxide, Small (2024) e2409043, https://doi.org/10.1002/smll.202409043.

[40]

D. Shi, S. Narayanan, K. Woeppel, et al., Improving the biocompatibility and functionality of neural interface devices with silica nanoparticles, Acc. Chem. Res 57 (2024) 1684-1695, https://doi.org/10.1021/acs.accounts.4c00160.

[41]

J.C. Hsieh, H. Alawieh, Y. Li, et al., A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface, Biosens. Bioelectron. 218 (2022) 114756, https://doi.org/10.1016/j.bios.2022.114756.

[42]

Z. Hu, Y. Liang, S. Fan, et al., Flexible neural interface from non-transient silk fibroin with outstanding conformality, biocompatibility, and bioelectric conductivity, Adv. Mater. 36 (2024) e2410007, https://doi.org/10.1002/adma.202410007.

[43]

D. Ando, T.F. Teshima, F. Zurita, et al., Filtration-processed biomass nanofiber electrodes for flexible bioelectronics, J. Nanobiotechnol. 20 (2022) 491, https://doi.org/10.1186/s12951-022-01684-3.

[44]

X. Yang, E. McGlynn, R. Das, et al., Nanotechnology enables novel modalities for neuromodulation, Adv. Mater. 33 (2021) e2103208, https://doi.org/10.1002/adma.202103208.

[45]

I. Zare, M.T. Yaraki, G. Speranza, et al., Gold nanostructures: synthesis, properties, and neurological applications, Chem. Soc. Rev. 51 (2022) 2601-2680, https://doi.org/10.1039/D1CS01111A.

[46]

X. Cheng, W. Li, Y. Wang, et al., Highly branched au superparticles as efficient photothermal transducers for optical neuromodulation, ACS Nano 18 (2024) 29572-29584, https://doi.org/10.1021/acsnano.4c07163.

[47]

V. Timosina, T. Cole, H. Lu, et al., A non-Newtonian liquid metal enabled enhanced electrography, Biosens. Bioelectron. 235 (2023) 115414, https://doi.org/10.1016/j.bios.2023.115414.

[48]

Z. Fekete, A. Zátonyi, A. Kaszás, et al., Transparent neural interfaces: challenges and solutions of microengineered multimodal implants designed to measure intact neuronal populations using highresolution electrophysiology and microscopy simultaneously, Micro Nanoeng. 9 (2023) 66, https://doi.org/10.1038/s41378-023-00519-x.

[49]

M.J. Antonini, A. Sahasrabudhe, A. Tabet, et al., Customizing MRI-compatible multifunctional neural interfaces through fiber drawing, Adv. Funct. Mater. 31 (2021) 2104857, https://doi.org/10.1002/adfm.202104857.

[50]

P.D.E. Baniqued, E.C. Stanyer, M. Awais, et al., Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review, J. Neuroeng. Rehabil. 18 (2021) 15, https://doi.org/10.1186/s12984-021-00820-8.

[51]

Y.T. Lo, M.J.R. Lim, C.Y. Kok, et al., Neural interface-based motor neuroprosthesis in poststroke upper limb neurorehabilitation: An individual patient data meta-analysis, Arch. Phys. Med. Rehabil. 105 (2024) 2336-2349, https://doi.org/10.1016/j.apmr.2024.04.001.

[52]

I. Nojima, H. Sugata, H. Takeuchi, et al., Brain-computer interface training based on brain activity can induce motor recovery in patients with stroke: A meta-analysis, Neurorehabil Neural Repair 36 (2022) 83-96, https://doi.org/10.1177/15459683211062895.

[53]

E.A.M. van Velthoven, O.C. van Stuijvenberg, D.R.E. Haselager, et al., Ethical implications of visual neuroprostheses-a systematic review, J. Neural Eng. 19 (2022) 026055, https://doi.org/10.1088/1741-2552/ac65b2.

[54]

S. Orlandi, S.C. House, P. Karlsson, et al., Brain-computer interfaces for children with complex communication needs and limited mobility: a systematic review, Front. Hum. Neurosci. 15 (2021) 643294, https://doi.org/10.3389/fnhum.2021.643294.

[55]

B. Peters, B. Eddy, D. Galvin-McLaughlin, et al., A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities, Front. Hum. Neurosci. 16 (2022) 952380, https://doi.org/10.3389/fnhum.2022.952380.

[56]

P.C. Tsai, A. Akpan, K.T. Tang, et al., Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review, BMC Geriatr. 25 (2025) 36, https://doi.org/10.1186/s12877-025-05676-4.

[57]

J.A. Cervantes, S. López, S. Cervantes, et al., Social robots and brain-computer interface video games for dealing with attention deficit hyperactivity disorder: a systematic review, Brain Sci 13 (2023) 1172, https://doi.org/10.3390/brainsci13081172.

[58]

A. Wang, X. Tian, D. Jiang, et al., Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: a randomized controlled trial, e4, Med 5 (2024) 559-569, https://doi.org/10.1016/j.medj.2024.02.014.

[59]

C. Bigoni, E. Beanato, S. Harquel, et al., Novel personalized treatment strategy for patients with chronic stroke with severe upper-extremity impairment: the first patient of the AVANCER trial, e3, Med 4 (2023) 591-599, https://doi.org/10.1016/j.medj.2023.06.006.

[60]

Z. Jadavji, J. Zhang, B. Paffrath, et al., Can children with perinatal stroke use a simple brain computer interface? Stroke 52 (2021) 2363-2370, https://doi.org/10.1161/strokeaha.120.030596.

[61]

G. Li, P. Huang, S. Cui, et al., Mechanisms of motor symptom improvement by long-term Tai Chi training in Parkinson's disease patients, Transl. Neurodegener. 11 (2022) 6, https://doi.org/10.1186/s40035-022-00280-7.

[62]

I. Brunner, C.B. Lundquist, A.R. Pedersen, et al., Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial, J. Neuroeng. Rehabil. 21 (2024) 10, https://doi.org/10.1186/s12984-024-01304-1.

[63]

J. Cantillo-Negrete, M.E. Rodríguez-García, P. Carrillo-Mora, et al., The ReHand-BCI trial: a randomized controlled trial of a brain-computer interface for upper extremity stroke neurorehabilitation, Front. Neurosci. 19 (2025) 1579988, https://doi.org/10.3389/fnins.2025.1579988.

[64]

L. Chen, B. Gu, Z. Wang, et al., EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: System design and clinical application, Front Med 15 (2021) 740-749, https://doi.org/10.1007/s11684-020-0794-5.

[65]

J. Fu, S. Chen, X. Shu, et al., Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study, Front. Neurosci. 17 (2023) 1146146, https://doi.org/10.3389/fnins.2023.1146146.

[66]

N. Guo, X. Wang, D. Duanmu, et al., SSVEP-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation, IEEE Trans. Neural Syst. Rehabil. Eng. 30 (2022) 1737-1744, https://doi.org/10.1109/TNSRE.2022.3185262.

[67]

M.S. Kim, H. Park, I. Kwon, et al., Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial, J. Neuroeng. Rehabil. 22 (2025) 1, https://doi.org/10.1186/s12984-024-01535-2.

[68]

D. Kong, Y. Chen, L. Wang, et al., Adoption of rehabilitation climbing wall combined with braincomputer fusion interface in adolescent idiopathic scoliosis, Alter. Ther. Health Med. 31 (2025) 208-215 https://pubmed.ncbi.nlm.nih.gov/38607193/.

[69]

R. Kumari, A. Dybus, M. Purcell, et al., Motor priming to enhance the effect of physical therapy in people with spinal cord injury, J. Spinal Cord. Med 23 (2024) 1-15, https://doi.org/10.1080/10790268.2024.2317011.

[70]

J. He, Z. Yuan, L. Quan, et al., Multimodal assessment of a BCI system for stroke rehabilitation integrating motor imagery and motor attempts: a randomized controlled trial, J. Neuroeng. Rehabil. 22 (2025) 185, https://doi.org/10.1186/s12984-025-01723-8.

[71]

S.H. Lee, S.S. Kim, B.H. Lee, Action observation training and brain-computer interface controlled functional electrical stimulation enhance upper extremity performance and cortical activation in patients with stroke: A randomized controlled trial, Physiother. Theory Pr. 38 (2022) 1126-1134, https://doi.org/10.1080/09593985.2020.1831114.

[72]

X. Li, L. Wang, S. Miao, et al., Sensorimotor rhythm-brain computer interface with audio-cue, motor observation and multisensory feedback for upper-limb stroke rehabilitation: a controlled study, Front. Neurosci. 16 (2022) 808830, https://doi.org/10.3389/fnins.2022.808830.

[73]

X. Liu, W. Zhang, W. Li, et al., Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial, BMC Neurol 23 (2023) 136, https://doi.org/10.1186/s12883-023-03150-5.

[74]

X. Luo, Effects of motor imagery-based brain-computer interface-controlled electrical stimulation on lower limb function in hemiplegic patients in the acute phase of stroke: a randomized controlled study, Front. Neurol. 15 (2024) 1394424, https://doi.org/10.3389/fneur.2024.1394424.

[75]

Z.Z. Ma, J.J. Wu, Z. Cao, et al., Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients, J. Neuroeng. Rehabil. 21 (2024) 91, https://doi.org/10.1186/s12984-024-01387-w.

[76]

Z.Z. Ma, J.J. Wu, X.Y. Hua, et al., Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis, Front. Neurol. 14 (2023) 1135466, https://doi.org/10.3389/fneur.2023.1135466.

[77]

M.A.L. Nicolelis, E.J.L. Alho, A.R.C. Donati, et al., Training with noninvasive brain-machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study, Sci. Rep. 12 (2022) 20545, https://doi.org/10.1038/s41598-022-24864-5.

[78]

M.E. Rodríguez-García, R.I. Carino-Escobar, P. Carrillo-Mora, et al., Neuroplasticity changes in cortical activity, grey matter, and white matter of stroke patients after upper extremity motor rehabilitation via a brain-computer interface therapy program, J. Neural Eng. 22 (2025) 026025, https://doi.org/10.1088/1741-2552/adbebf.

[79]

B. Svejgaard, B. Modrau, J.J. Hernández-Gloria, et al., Associative brain-computer interface training increases wrist extensor corticospinal excitability in patients with subacute stroke, J. Neurophysiol. 133 (2025) 333-341, https://doi.org/10.1152/jn.00452.2024.

[80]

C. Wan, Q. Zhang, Y. Qiu, et al., Effects of dual-task mode brain-computer interface based on motor imagery and virtual reality on balance and attention in patients with stroke: a randomized controlled pilot trial, J. Neuroeng. Rehabil. 22 (2025) 187, https://doi.org/10.1186/s12984-025-01730-9.

[81]

P. Wang, J. Liu, L. Wang, et al., Effects of brain-computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial, Front. Psychol. 14 (2023) 1241081, https://doi.org/10.3389/fpsyg.2023.1241081.

[82]

B. Weisinger, D.P. Pandey, J.L. Saver, et al., Frequency-tuned electromagnetic field therapy improves post-stroke motor function: a pilot randomized controlled trial, Front. Neurol. 13 (2022) 1004677, https://doi.org/10.3389/fneur.2022.1004677.

[83]

Z. Yuan, Y. Peng, L. Wang, et al., Effect of BCI-controlled pedaling training system with multiple modalities of feedback on motor and cognitive function rehabilitation of early subacute stroke patients, IEEE Trans. Neural Syst. Rehabil. Eng. 29 (2021) 2569-2577, https://doi.org/10.1109/TNSRE.2021.3132944.

[84]

Z. Ming, W. Yu, J. Fan, et al., Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke, Sci. Rep. 15 (2025) 11829, https://doi.org/10.1038/s41598-025-96039-x.

[85]

C.G. Zhao, F. Ju, W. Sun, et al., Effects of training with a brain-computer interface-controlled robot on rehabilitation outcome in patients with subacute stroke: A randomized controlled trial, Neurol. Ther. 11 (2022) 679-695, https://doi.org/10.1007/s40120-022-00333-z.

[86]

H. Lorach, A. Galvez, V. Spagnolo, et al., Walking naturally after spinal cord injury using a brain-spine interface, Nature 618 (2023) 126-133, https://doi.org/10.1038/s41586-023-06094-5.

[87]

D. Lewis, Brain-spine interface allows paralysed man to walk using his thoughts, Nature 618 (2023) 18, https://doi.org/10.1038/d41586-023-01728-0.

[88]

N.S. Card, M. Wairagkar, C. Iacobacci, et al., An accurate and rapidly calibrating speech neuroprosthesis, N. Engl. J. Med 391 (2024) 609-618, https://doi.org/10.1056/NEJMoa2314132.

[89]

M.J. Vansteensel, E.G.M. Pels, M.G. Bleichner, et al., Fully implanted brain-computer interface in a locked-in patient with ALS, N. Engl. J. Med 375 (2016) 2060-2066, https://doi.org/10.1056/NEJMoa1608085.

[90]

D.B. Adhia, R. Mani, P.R. Turner, et al., Infraslow neurofeedback training alters effective connectivity in individuals with chronic low back pain: a secondary analysis of a pilot randomized placebo-controlled study, Brain Sci 12 (2022) 1514, https://doi.org/10.3390/brainsci12111514.

[91]

J. Mathew, D.B. Adhia, M.L. Smith, et al., Closed-loop infraslow brain-computer interface can modulate cortical activity and connectivity in individuals with chronic painful knee osteoarthritis: A secondary analysis of a randomized placebo-controlled clinical trial, Clin. EEG Neurosci. 56 (2025) 165-180, https://doi.org/10.1177/15500594241264892.

[92]

X.N. Wang, T. Zhang, B.C. Han, et al., Wearable EEG neurofeedback based-on machine learning algorithms for children with autism: A randomized, placebo-controlled study, Curr. Med Sci. 44 (2024) 1141-1147, https://doi.org/10.1007/s11596-024-2938-3.

[93]

L. Anderson, D. De Ridder, P. Glue, et al., A safety and feasibility randomized placebo controlled trial exploring electroencephalographic effective connectivity neurofeedback treatment for fibromyalgia, Sci. Rep. 15 (2025) 209, https://doi.org/10.1038/s41598-024-83776-8.

[94]

K. Andrade, N. Houmani, T. Guieysse, et al., Self-modulation of gamma-band synchronization through EEG-neurofeedback training in the elderly, J. Integr. Neurosci. 23 (2024) 67, https://doi.org/10.31083/j.jin2303067.

[95]

C. Annaheim, K. Hug, C. Stumm, et al., Neurofeedback in patients with frontal brain lesions: a randomized, controlled double-blind trial, Front. Hum. Neurosci. 16 (2022) 979723, https://doi.org/10.3389/fnhum.2022.979723.

[96]

A.M. Brewe, L. Antezana, C.N. Carlton, et al., A randomized trial utilizing EEG brain computer interface to improve facial emotion recognition in autistic adults, J. Autism Dev. Disord. 55 (2025) 3217-3230, https://doi.org/10.1007/s10803-024-06436-w.

[97]

Y. He, Z. Tang, G. Sun, et al., Effectiveness of a mindfulness meditation app based on an electroencephalography-based brain-computer interface in radiofrequency catheter ablation for patients with atrial fibrillation: pilot randomized controlled trial, JMIR Mhealth Uhealth 11 (2023) e44855, https://doi.org/10.2196/44855.

[98]

C.R. Oehrn, S. Cernera, L.H. Hammer, et al., Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson's disease: A blinded randomized feasibility trial, Nat. Med 30 (2024) 3345-3356, https://doi.org/10.1038/s41591-024-03196-z.

[99]

O. Pino, A randomized controlled trial (RCT) to explore the effect of audio-visual entrainment among psychological disorders, Acta Biomed 92 (2022) e2021408, https://doi.org/10.23750/abm.v92i6.12089.

[100]

S. Prinsloo, T.J. Kaptchuk, D. De Ridder, et al., Brain-computer interface relieves chronic chemotherapy-induced peripheral neuropathy: A randomized, double-blind, placebo-controlled trial, Cancer 130 (2024) 300-311, https://doi.org/10.1002/cncr.35027.

[101]

S. Marceglia, C. Conti, O. Svanidze, et al., Double-blind cross-over pilot trial protocol to evaluate the safety and preliminary efficacy of long-term adaptive deep brain stimulation in patients with Parkinson's disease, BMJ Open 12 (2022) e049955, https://doi.org/10.1136/bmjopen-2021-049955.

[102]

Medtronic, Groundbreaking study published in the Journal of the American Medical Association (JAMA) Neurology demonstrates effectiveness of Medtronic BrainSenseTM Adaptive deep brain stimulation for people with Parkinson's. https://news.medtronic.com/Groundbreaking-study-published-in-the-Journal-of-the-American-Medical-Association-JAMA-Neurology-demonstrates-effectiveness-of-Medtronic-BrainSense-TM-Adaptive-deepbrain-stimulation-for-people-with-Parkinsons, 2025 (accessed 25 September 2025).

[103]

H.M. Bronte-Stewart, M. Beudel, J.L. Ostrem, et al., Long-term personalized adaptive deep brain stimulation in Parkinson disease: A nonrandomized clinical trial, JAMA Neurol 82 (2025) 1171-1180, https://doi.org/10.1001/jamaneurol.2025.2781.

[104]

D. Demontis, G.B. Walters, G. Athanasiadis, et al., Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains, Nat. Genet 55 (2023) 198-208, https://doi.org/10.1038/s41588-022-01285-8.

[105]

T.D. Als, M.I. Kurki, J. Grove, et al., Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses, Nat. Med 29 (2023) 1832-1844, https://doi.org/10.1038/s41591-023-02352-1.

[106]

J. Zou, H. Chen, X. Chen, et al., Noninvasive closed-loop acoustic brain-computer interface for seizure control, Theranostics 14 (2024) 5965-5981, https://doi.org/10.7150/thno.99820.

[107]

L. Wang, T. Zhang, J. Lei, et al., A biodegradable and restorative peripheral neural interface for the interrogation of neuropathic injuries, Nat. Commun. 16 (2025) 1716, https://doi.org/10.1038/s41467-025-56089-1.

[108]

M.S. Willsey, N.P. Shah, D.T. Avansino, et al., A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis, Nat. Med 31 (2025) 96-104, https://doi.org/10.1038/s41591-024-03341-8.

[109]

F. Yu, Z. Rao, N. Chen, et al., ArmBCIsys: Robot arm BCI system with time-frequency network for multiobject grasping, IEEE Trans. Neural Netw. Learn Syst 36 (2025) 18327-18341, https://doi.org/10.1109/TNNLS.2025.3579332.

[110]

M. Kritsidima, S. Scambler, K. Asimakopoulou, Exploring the levels of dental anxiety in Greek patients, Int. Dent. J. 75 (2025) 100826, https://doi.org/10.1016/j.identj.2025.04.006.

[111]

G. Sun, F. Zeng, M. McCartin, et al., Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents, Sci. Transl. Med. 14 (2022) eabm5868, https://doi.org/10.1126/scitranslmed.abm5868.

[112]

H. Knotkova, C. Hamani, E. Sivanesan, et al., Neuromodulation for chronic pain, Lancet 397 (2021) 2111-2124, https://doi.org/10.1016/S0140-6736(21)00794-7.

[113]

B. Saxena, M. Goswami, S. Singh, et al., Comparative evaluation of the effectiveness of transcutaneous electric nerve stimulation and photobiostimulation using diode laser prior to local anesthesia administration in children undergoing bilateral orthodontic extraction-a randomized controlled clinical trial, Lasers Med. Sci. 40 (2025) 177, https://doi.org/10.1007/s10103-025-04440-9.

[114]

J. Wang, Z.S. Chen, Closed-loop neural interfaces for pain: where do we stand? Cell Rep. Med. 5 (2024) 101662, https://doi.org/10.1016/j.xcrm.2024.101662.

[115]

C. Takizawa, E. Gemmell, J. Kenworthy, et al., A systematic review of the prevalence of oropharyngeal dysphagia in stroke, Parkinson's disease, Alzheimer's disease, head injury, and pneumonia, Dysphagia 31 (2016) 434-441, https://doi.org/10.1007/s00455-016-9695-9.

[116]

K.G. Oweiss, I.S. Badreldin, Neuroplasticity subserving the operation of brain-machine interfaces, Neurobiol. Dis. 83 (2015) 161-171, https://doi.org/10.1016/j.nbd.2015.05.001.

[117]

F.R. Willett, E.M. Kunz, C. Fan, et al., A high-performance speech neuroprosthesis, Nature 620 (2023) 1031-1036, https://doi.org/10.1038/s41586-023-06377-x.

[118]

S.D. Stavisky, Restoring speech using brain-computer interfaces, Annu Rev. Biomed. Eng. 27 (2025) 29-54, https://doi.org/10.1146/annurev-bioeng-110122-012818.

[119]

M. Wairagkar, N.S. Card, T. Singer-Clark, et al., An instantaneous voice-synthesis neuroprosthesis, Nature 644 (2025) 145-152, https://doi.org/10.1038/s41586-025-09127-3.

[120]

P. Magee, M. Ienca, N. Farahany, Beyond neural data: Cognitive biometrics and mental privacy, Neuron 112 (2024) 3017-3028, https://doi.org/10.1016/j.neuron.2024.09.004.

[121]

M. Eversdijk, M. Habibović, D.L. Willems, et al., Ethics of wearable-based out-of-hospital cardiac arrest detection, Circ. Arrhythm. Electrophysiol. 17 (2024) e012913, https://doi.org/10.1161/CIRCEP.124.012913.

[122]

E.C. Winkler, B.M. Knoppers, Ethical challenges of precision cancer medicine, Semin Cancer Biol 84 (2022) 263-270, https://doi.org/10.1016/j.semcancer.2020.09.009.

[123]

L. Goncharov, H. Suominen, M. Cook, Dynamic consent and personalised medicine, Med J. Aust. 216 (2022) 547-549, https://doi.org/10.5694/mja2.51555.

[124]

C.M. Parobek, M.M. Thorsen, P. Has, et al., Video education about genetic privacy and patient perspectives about sharing prenatal genetic data: A randomized trial, Am. J. Obstet. Gynecol. 227 (2022) 87.e1-87.e13, https://doi.org/10.1016/j.ajog.2022.03.047.

[125]

UNESCO, Ethics of Neurotechnology. https://www.unesco.org/en/ethics-neurotech, 2025 (accessed 6 March 2025).

[126]

N. Martinez-Martin, Z. Luo, A. Kaushal, et al., Ethical issues in using ambient intelligence in healthcare settings, Lancet Digit Health 3 (2021) e115-e123, https://doi.org/10.1016/S2589-7500(20)30275-2.

[127]

K. Lam, M.D. Abràmoff, J.M. Balibrea, et al., A Delphi consensus statement for digital surgery, NPJ Digit. Med. 5 (2022) 100, https://doi.org/10.1038/s41746-022-00641-6.

[128]

M.S. Okun, T. Marjenin, J. Ekanayake, et al., Definition of implanted neurological device abandonment: a systematic review and consensus statement, JAMA Netw. Open 7 (2024) e248654, https://doi.org/10.1001/jamanetworkopen.2024.8654.

[129]

The U.S. Food and Drug Administration, Classify Your Medical Device. https://www.fda.gov/medical-devices/overview-device-regulation/classify-your-medical-device, 2026 (accessed 20 January 2026).

[130]

The U.S. Food and Drug Administration, Premarket Approval (PMA). https://www.fda.gov/medical-devices/premarket-submissions-selecting-and-preparing-correctsubmission/premarket-approval-pma, 2025 (accessed 6 March 2025).

[131]

European Commission, Regulation (EU) 2017/745 on Medical Devices. https://single-market-economy.ec.europa.eu/single-market/european-standards/harmonisedstandards/medical-devices_en, 2025 (accessed 6 March 2025).

[132]

National Medical Products Administration (NMPA), Medical Device Regulations in China. https://www.nmpa.gov.cn, 2025 (accessed 6 March 2025).

[133]

Pharmaceuticals and Medical Devices Agency (PMDA), Medical Device Regulations in Japan. https://www.pmda.go.jp, 2025 (accessed 6 March 2025).

[134]

J. Villa, J. Cury, L. Kessler, et al., Enhancing biocompatibility of the brain-machine interface: a review, Bioact. Mater. 42 (2024) 531-549, https://doi.org/10.1016/j.bioactmat.2024.08.034.

[135]

L. Wang, C. Zhang, Z. Hao, et al., Bioaugmented design and functional evaluation of low damage implantable array electrodes, Bioact. Mater. 47 (2025) 18-31, https://doi.org/10.1016/j.bioactmat.2024.12.033.

[136]

The U.S. Food and Drug Administration, Breakthrough Devices Program. https://www.fda.gov/medical-devices/how-study-and-market-your-device/breakthrough-devices-program, 2025 (accessed 6 March 2025).

[137]

International Organization for Standardization (ISO), ISO/IEC JTC 1/SC 43 Brain-computer interfaces. https://www.iso.org/committee/9082407.html, 2025 (accessed 6 March 2025).

[138]

IEEE Standards Association, Neurotechnologies for Brain-Machine Interfacing. https://standards.ieee.org/industry-connections/activities/neurotechnologies-for-brain-machine-interfacing/, 2025 (accessed 6 March 2025).

[139]

M. Ienca, G. Valle, S. Raspopovic, Clinical trials for implantable neural prostheses: understanding the ethical and technical requirements, Lancet Digit Health 7 (2025) e216-e224, https://doi.org/10.1016/S2589-7500(24)00222-X.

[140]

Y. Ma, Y. Liu, L. Chen, et al., BrainCLIP: brain representation via CLIP for generic natural visual stimulus decoding, IEEE Trans. Med. Imaging 44 (2025) 3962-3972, https://doi.org/10.1109/TMI.2025.3537287.

[141]

Y. Li, Y. Wang, B. Lei, et al., SCDM: Unified representation learning for EEG-to-fNIRS cross-modal generation in MI-BCIs, IEEE Trans. Med Imaging 44 (2025) 2384-2394, https://doi.org/10.1109/TMI.2025.3532480.

[142]

K. Liu, T. Yang, Z. Yu, et al., MSVTNet: Multi-scale vision transformer neural network for EEGbased motor imagery decoding, IEEE J. Biomed. Health Inf 28 (2024) 7126-7137, https://doi.org/10.1109/JBHI.2024.3450753.

[143]

R. Zhang, S. Feng, N. Hu, et al., Hybrid brain-computer interface controlled soft robotic glove for stroke rehabilitation, IEEE J. Biomed. Health Inf. 28 (2024) 4194-4203, https://doi.org/10.1109/JBHI.2024.3392412.

[144]

G. Cheng, S.K. Ehrlich, M. Lebedev, et al., Neuroengineering challenges of fusing robotics and neuroscience, Sci. Robot. 5 (2020) eabd1911, https://doi.org/10.1126/scirobotics.abd1911.

[145]

M.J. Vansteensel, S. Leinders, M.P. Branco, et al., Longevity of a brain-computer interface for amyotrophic lateral sclerosis, N. Engl. J. Med 391 (2024) 619-626, https://doi.org/10.1056/NEJMoa2314598.

[146]

U. S. Government Accountability Office, Technology Assessment. Brain-Computer Interfaces. Applications, Challenges, and Policy Options. GAO-25-106952. https://www.gao.gov, 2025 (accessed 6 March 2025).

[147]

A. Wexler, A. Feinsinger, Ethical challenges in translating brain-computer interfaces, Nat. Hum. Behav. 8 (2024) 1831-1833, https://doi.org/10.1038/s41562-024-01972-y.

[148]

Federal Register, Medicare Program; Medicare Coverage of Innovative Technology (MCIT) and Definition of “Reasonable and Necessary.” https://www.federalregister.gov/documents/2021/01/14/2021-00707/medicare-program-medicare-coverageof-innovative-technology-mcit-and-definition-of-reasonable-and, 2025 (accessed 6 March 2025).

[149]

X. Wang, M. Xu, H. Yang, et al., Ultraflexible neural electrodes enabled synchronized long-term dopamine detection and wideband chronic recording deep in brain, ACS Nano 18 (2024) 34272-34287, https://doi.org/10.1021/acsnano.4c12429.

[150]

K. Patch, Neural dust swept up in latest leap for bioelectronic medicine, Nat. Biotechnol. 39 (2021) 255-256, https://doi.org/10.1038/s41587-021-00856-0.

[151]

C. Chai, X. Yang, Y. Zheng, et al., Multimodal fusion of magnetoencephalography and photoacoustic imaging based on optical pump: trends for wearable and noninvasive brain-computer interface, Biosens. Bioelectron. 278 (2025) 117321, https://doi.org/10.1016/j.bios.2025.117321.

[152]

S. Moreno-Calderón, V. Martínez-Cagigal, E. Santamaría-Vázquez, et al., Combining braincomputer interfaces and multiplayer video games: an application based on c-VEPs, Front. Hum. Neurosci. 17 (2023) 1227727, https://doi.org/10.3389/fnhum.2023.1227727.

[153]

S. Xu, Y. Liu, H. Lee, et al., Neural interfaces: bridging the brain to the world beyond healthcare, Exploration 4 (2024) 20230146, https://doi.org/10.1002/EXP.20230146.

PDF (6309KB)

7

Accesses

0

Citation

Detail

Sections
Recommended

/