Introduction
What are brain-computer interfaces, and how do they work?
Key technologies behind BCIs: EEG, fNIRS, implanted electrodes
Medical applications: From ALS to stroke rehabilitation
Case study: Neuralink, Synchron, and other industry leaders
Regulatory and ethical considerations in clinical BCI use
The future of neurotechnology in healthcare
What if a paralyzed patient could communicate just by thinking, or control a prosthetic limb with their mind? Brain-computer interfaces (BCIs) are making this a reality, turning neural activity into a therapeutic pathway in which neurological signals can bypass damaged pathways, offering new hope in neurological care.1,2
Image Credit: Gorodenkoff/Shutterstock.com
Introduction
BCIs mark a paradigm shift in neurotherapeutics, where cognition and intent can directly influence external digital and mechanical systems. As the technology matures, BCIs are becoming central to efforts aimed at restoring autonomy in individuals affected by neurological injuries and diseases.
These systems translate neural activity into commands that result in action, bypassing damaged motor pathways and empowering patients with severe motor disabilities.2,3
As research in neuroscience, signal processing, and biomedical engineering converges, BCIs are transitioning from experimental prototypes to viable medical interventions with tangible clinical impact.
Their applications now span neurorehabilitation, assistive communication, and the restoration of sensory or motor function.
This article explores the various technologies employed in the development of BCIs, their applications in clinical settings, and the regulatory and ethical concerns associated with BCIs while highlighting both their transformative potential and the challenges that lie ahead.
Can Neuroplasticity Be Hacked? Exploring the Limits
What are brain-computer interfaces, and how do they work?
A BCI system decodes brain signals to allow users to control external devices without muscular movement. The process typically includes five stages — signal acquisition, preprocessing, feature extraction, classification, and control output.1,4
Signal acquisition is performed using technologies such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), or implanted electrodes. This raw data, often embedded with noise and artifacts, undergoes signal processing to enhance relevant signals while filtering out interference.4
Next, feature extraction identifies distinctive patterns that correspond to the user's intended action or thought. These features are then classified using algorithms, often based on machine learning, which translates them into digital commands.
Finally, device control relays these commands to an external system, enabling the user to interact with computers, robotic limbs, communication aids, or other assistive technologies.3,4
Thought-Controlled Prosthetics: A Brain-Computer Interface Breakthrough
Key technologies behind BCIs: EEG, fNIRS, implanted electrodes
Among the most commonly used non-invasive technologies in BCIs is EEG, which records electrical activity from the scalp. EEG is widely used in both clinical and research settings due to its high temporal resolution, portability, and relatively low cost.1
In BCI applications, EEG enables users to perform tasks such as cursor control, speller selection, and robotic limb operation by detecting characteristic brainwave patterns, such as event-related potentials (ERPs), sensorimotor rhythms (SMRs), or steady-state visual evoked potentials (SSVEPs).1,5
These patterns are typically elicited through specific cognitive or motor imagery tasks and then decoded using real-time signal processing and classification algorithms. However, despite its advantages, EEG is limited by its low spatial resolution and vulnerability to noise and motion artifacts, which can affect signal clarity and overall system reliability.1,5
Functional near-infrared spectroscopy, or fNIRS, detects changes in cerebral blood flow by measuring light absorption, providing better spatial resolution and robustness to electrical noise.
It is especially useful in detecting cognitive tasks localized in the prefrontal cortex, such as mental arithmetic and decision-making, where the scope of traditional EEG is limited.1,2,4
Because fNIRS is less susceptible to motion and electrical artifacts, it is often employed in mobile and hybrid BCI applications, including wearable systems for neurofeedback and rehabilitation. However, its low temporal resolution limits real-time application, especially in tasks requiring immediate system responsiveness.2,4
To overcome the limitations of non-invasive methods such as EEG and fNIRS, invasive BCIs such as implanted electrodes are being explored. These include microelectrode arrays that penetrate the cortex to capture high-resolution neuronal data and electrocorticography (ECoG), where electrodes are placed on the cortical surface.1
A novel advancement in this area is the use of endovascular electrodes, such as the Stentrode or Stent-electrode recording array, which is a small stent-mounted electrode array developed by Synchron that can be permanently implanted into a blood vessel in the brain, without the need for craniotomy, while providing stable signal quality.6
Brain-Computer Interfaces: What They Are and Why They Matter
Medical applications: From ALS to stroke rehabilitation
In clinical medicine, BCIs are most notably applied in assistive communication for patients with amyotrophic lateral sclerosis (ALS) and brainstem stroke. Such systems enable users to type or control digital devices solely through thought, dramatically enhancing the quality of life.7
In stroke rehabilitation, BCIs can promote motor recovery through neurofeedback training, where imagined or attempted movements are reinforced via visual or haptic feedback, thereby encouraging neuroplasticity. 4
Furthermore, initiatives funded by the United States (U.S.) Department of Defense’s Defense Advanced Research Projects Agency, or DARPA, has shown the potential of BCIs to control neuroprosthetics, restore tactile sensation, and enhance cognitive training.8
BCIs are also being explored for managing neuropsychiatric conditions such as depression and attention-deficit/hyperactivity disorder (ADHD), offering a route to modulate dysfunctional brain networks through closed-loop stimulation.1,2
Case study: Neuralink, Synchron, and other industry leaders
Neuralink, founded in 2016 by Elon Musk, has garnered significant attention for its ambitious development of high-bandwidth intracortical BCIs using ultra-thin electrodes and robotic implantation. Early demonstrations include enabling quadriplegic individuals to control devices and restoring sensory input.9
However, ethical concerns regarding informed consent, data privacy, and the commercialization of neurotechnologies have surrounded some of the research emerging from Neuralink.
Synchron's Stentrode device offers a less invasive alternative by deploying electrodes endovascularly into the superior sagittal sinus. Its safety and efficacy were demonstrated in the Stentrode With Thought-Controlled Digital Switch (SWITCH) trial, where participants used the BCI to control digital interfaces with no major adverse events reported.6
Meanwhile, BrainGate Inc., owned by Tufts University, has produced the most extensive long-term clinical data for intracortical BCIs. Over thousands of device days, the neural interface system developed by BrainGate showed consistent safety and enabled participants to achieve functional goals like communication and environmental control.7
Regulatory and ethical considerations in clinical BCI use
The clinical use of BCIs raises significant ethical and regulatory questions. Issues such as autonomy, cognitive liberty, and data ownership still need to be addressed, particularly for bidirectional BCIs that can stimulate as well as record brain activity.10
While the U.S. Food and Drug Administration (FDA) has granted breakthrough status to several BCI technologies, including Synchron’s Stentrode system, a comprehensive regulatory framework is lacking.
Standardized guidelines are needed for device safety, long-term implantation, cybersecurity, and informed consent. Moreover, equitable access and prevention of misuse must be prioritized as the capabilities and applications of BCIs expand.9
What is Synaptic Pruning?
The future of neurotechnology in healthcare
The pace of the current research on BCIs indicates that neural interface technology is expected to become more sophisticated through advances in artificial intelligence, sensor miniaturization, and wireless communication.
Hybrid systems integrating EEG, fNIRS, and implanted electrodes will improve comprehensive brain state monitoring, while cloud-based processing will allow for adaptive, personalized therapies.2
Furthermore, researchers envision BCIs facilitating the restoration of lost senses, enhancing cognitive function, and even forming part of everyday healthcare systems. However, the ethical challenges of cognitive enhancement and the potential for surveillance or coercion require thorough and critical analysis and policy development to safeguard the autonomy and privacy of users.2,10
Additionally, future BCI applications may include the integration of neuroprosthetics with sensory feedback loops, closed-loop neuromodulation for psychiatric disorders, and immersive brain-controlled virtual environments for therapy.
Cross-disciplinary collaboration between neuroscientists, engineers, ethicists, and clinicians will be essential to ensure the responsible deployment of this transformative technology.2,9,10
To conclude, BCIs represent a confluence of neurotechnology, clinical innovation, and ethical deliberation. As systems improve in safety, performance, and usability, their role in medicine will only grow. From restoring lost communication in paralyzed patients to reshaping post-stroke rehabilitation, BCIs are unlocking new frontiers in therapeutic intervention.
However, the road ahead requires responsible, robust research and inclusive design to ensure that the benefits of BCIs are accessible, equitable, and aligned with societal values and ethical codes.
References
- Kawala-Sterniuk, A., Browarska, N., Al-Bakri, A., Pelc, M., Zygarlicki, J., Sidikova, M., Martinek, R., & Gorzelanczyk, E. J. (2021). Summary of over Fifty Years with Brain-Computer Interfaces-A Review. Brain Sciences, 11(1), 43. DOI:10.3390/brainsci11010043
- Liu, Z., Shore, J., Wang, M., Yuan, F., Buss, A., & Zhao, X. (2021). A systematic review on hybrid EEG/fNIRS in brain-computer interface. Biomedical Signal Processing and Control, 68, 102595. DOI:10.1016/j.bspc.2021.102595
- Shih, J. J., Krusienski, D. J., & Wolpaw, J. R. (2012). Brain-computer interfaces in medicine. Mayo Clinic Proceedings, 87(3), 268–279. DOI:10.1016/j.mayocp.2011.12.008
- Naseer, N., & Hong, K. S. (2015). fNIRS-based brain-computer interfaces: a review. Frontiers in Human Neuroscience, 9, 3. DOI:10.3389/fnhum.2015.00003
- Fontanillo Lopez, C. A., Li, G., & Zhang, D. (2020). Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects. Frontiers in Neuroscience, 14, 611130. DOI:10.3389/fnins.2020.611130
- Mitchell, P., Lee, S. C. M., Yoo, P. E., Morokoff, A., Sharma, R. P., Williams, D. L., MacIsaac, C., … Campbell, B. C. V. (2023). Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study. JAMA Neurology, 80(3), 270–278. DOI:10.1001/jamaneurol.2022.4847
- Rubin, D. B., Ajiboye, A. B., Barefoot, L., Bowker, M., Cash, S. S., Chen, D., Donoghue, … Hochberg, L. R. (2023). Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System. Neurology, 100(11), e1177–e1192. DOI:10.1212/WNL.0000000000201707
- Miranda, R. A., Casebeer, W. D., Hein, A. M., Judy, J. W., Krotkov, E. P., Laabs, T. L., Manzo, J. E., Pankratz, K. G., Pratt, G. A., Sanchez, J. C., Weber, D. J., Wheeler, T. L., & Ling, G. S. (2015). DARPA-funded efforts in the development of novel brain-computer interface technologies. Journal of Neuroscience Methods, 244, 52–67. DOI:10.1016/j.jneumeth.2014.07.019
- Lavazza, A., Balconi, M., Marcello Ienca, Minerva, F., Pizzetti, F. G., Reichlin, M., Samorè, F., Sironi, V. A., Navarro, M. S., & Songhorian, S. (2025). Neuralink’s brain-computer interfaces: medical innovations and ethical challenges. Frontiers in Human Dynamics, 7. DOI:10.3389/fhumd.2025.1553905
- Gordon, E. C., & Seth, A. K. (2024). Ethical considerations for the use of brain-computer interfaces for cognitive enhancement. PLoS Biology, 22(10), e3002899. DOI:10.1371/journal.pbio.3002899
Further Reading