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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64048
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer science and Engineering
RTC Institute of Technology Ranchi
Department of Computer science and Engineering
RTC Institute of Technology Ranchi
Department of Computer science and Engineering
RTC Institute of Technology Ranchi
Department of Computer science and Engineering
RTC Institute of Technology Ranchi
Department of Computer science and Engineering
RTC Institute of Technology Ranchi
Department of Computer science and Engineering
RTC Institute of Technology Ranchi
Brain-Computer Interfaces (BCIs) represent one of the most transformative technological advancements of the 21st century, enabling direct communication between the human brain and external devices. These systems are broadly categorized into invasive BCIs, which involve surgical implantation of electrodes into the brain, and non-invasive BCIs, which utilize external sensors such as EEG headsets. While BCIs offer revolutionary applications in healthcare, gaming, education, defense, and assistive technologies, they also generate highly sensitive neural data that raises serious privacy concerns. This paper presents a comparative study of data privacy measures in invasive and non-invasive BCIs. It examines data collection methods, security vulnerabilities, regulatory challenges, ethical concerns, and technical safeguards. The study finds that invasive BCIs require stronger privacy protection due to deeper neural access and continuous monitoring, whereas non-invasive BCIs face broader consumer-data risks because of easier adoption and cloud-based ecosystems. Brain data is the most intimate form of data; however, security protocols often lag behind hardware innovation. Recommendations are proposed for encryption, consent management, anonymization, zero-trust architectures, and international policy frameworks.
KEYWORDS
Brain-Computer Interfaces are reshaping human-machine interaction, but they also introduce unprecedented privacy challenges. Invasive BCIs present critical risks due to direct brain access and continuous monitoring, while non-invasive BCIs create scalable privacy threats through wearable ecosystems and consumer platforms. A comparative approach shows that privacy strategies must differ based on device invasiveness, signal depth, and deployment context. Strong encryption, transparent consent, user ownership of neural data, and dedicated neuro-rights legislation are essential for responsible BCI adoption. Future research should focus on privacy-preserving AI models and international standards for neural data governance.
[1] Wang, Y., Ge, M., & Xu, S. (2026). Advances in brain–computer interfaces (BCI): Challenges and opportunities. Biomimetics, 11(2), 157. https://doi.org/10.3390/biomimetics11020157. [2]New America. (2026). Brain-computer interfaces: Fundamentals and applications in commercial and medical contexts. https://www.newamerica.org/insights/the-rise of- neurotech-and-the-risks-for-our-brain-data/ [3]Panda International. (2025). BCI breakthroughs: Connecting brains and technology. https://www.panda-int.com/insights/inside-the-bci-race-how-a-global-push-to-bridge-minds-and-machines-is-taking-shape/ [4]Persistence Market Research. (2026). Brain computer interface market size, share, and growth forecast, 2026 – 2033. https://www.persistencemarketresearch.com/market-research/brain-computer-interface-market.asp [5]PMC. (2023). Brain–computer interface: trend, challenges, and threats. PMC, 10403483. https://pmc.ncbi.nlm.nih.gov/articles/PMC10403483/
Publication Date
5/26/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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