Volume 14 , Issue 1 , PP: 31-44, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Bushra Khatoon Zaidi 1 , Meena Chaudhary 2 , Javed Wasim 3 , Prabhat Kumar Srivastava 4 , Aditi Sharma 5 * , Atifa Arshad 6
Doi: https://doi.org/10.54216/JISIoT.140103
One example of a cutting-edge technology that is opening up new channels for human-machine connection is brain-computer interfaces, or BCIs. From keyboards and mice to touchscreens, voice commands, and gesture engagements, communication interfaces have evolved over time. New methods of controlling computer systems and engaging with virtual worlds have gained appeal as computers become more and more ingrained in daily life. These innovative applications range from gaming to teaching. It's important to handle ethical, privacy, and security issues related to developing and applying Brain-Computer Interface (BCI) technology from a balanced standpoint. Susceptible brain signals must be gathered and interpreted for BCI devices. Unauthorized access to this material carries the risk of compromising privacy by disclosing private thoughts, feelings, or other sensitive information. The initial areas of brain-computer interface (BCI) applications were based on EEG and created for medical use, hoping to help patients get back to their regular lives. Beyond the original purpose, EEG-based BCI applications have become more and more important in the non-medical field, helping healthy individuals live better lives by becoming more productive, collaborative, and self-developing, for example.
Ethics , Privacy , Telepathy , Memory , Accuracy , Complexity
[1] Das, A., & Ray, S. (2024). Techno-humane futures in the global south: lessons from ProfessorShonku. Technovation, 132,102987–102987. ttps://doi.org/10.1016/j.technovation.2024.102987
[2] B. Maiseliet al., “Brain–computer interface: Trend, Challenges, and Threats,” Brain Informatics, vol. 10, no. 1, p. 20, Aug. 2023.
[3] Goar, V., Sharma, A., Yadav, N.S. et al. IoT-Based Smart Mask Protection against the Waves of COVID-19. J Ambient Intell Human Comput 14, 11153–11164 (2023). https://doi.org/10.1007/s12652-022-04395-7
[4] How Modern Distributors Can Ensure A Highly-Trained And Capable Workforce. (2022, December 5). Amshuhu. https://amshuhu.com/how-modern-distributors-can-ensure-a-highly-trained-and-capable-workforce/
[5] K. Lavarone, “Somatosensory Evoked Potential (SEP) test: What to Know,” www.medicalnewstoday.com, Oct. 12, 2022.
[6] Houssein, E. H., Hammad, A., & Ali, A. A. (2022). Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. Neural Computing and Applications. https://doi.org/10.1007/s00521-022-07292-4
[7] Han, Q., Yue, L., Gao, F., Zhang, L., Hu, L., & Feng, Y. (2021). The Prediction of Acute Postoperative Pain Based on Neural Oscillations Measured before the Surgery. Neural Plasticity, 2021, 1–12. https://doi.org/10.1155/2021/5543974
[8] Z. Cao, “A Review of Artificial Intelligence for EEG‐based Brain−computer Interfaces and Applications,” Brain Science Advances, vol. 6, no. 3, pp. 162–170, Sep. 2020.
[9] Kögel, J., Jox, R. J., & Friedrich, O. (2020). What is it like to use a BCI? – insights from an interview study with brain-computer interface users. BMC Medical Ethics, 21(1). https://doi.org/10.1186/s12910-019-0442-2
[10] Ankit Kumar, Kamred Udham Singh, Pankaj Dadheech, Aditi Sharma, Ahmed I. Alutaibi, Ahed Abugabah, Arwa Mohsen Alawajy, Enhanced Route navigation control system for turtlebot using human-assisted mobility and 3-D SLAM optimization, Heliyon,Volume 10, Issue 5,2024,e26828,ISSN 2405-8440,https://doi.org/10.1016/j.heliyon.2024.e26828.
[11] N. Padfield, J. Zabalza, H. Zhao, V. Masero, and J. Ren, “EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges,” Sensors, vol. 19, no. 6, p. 1423, Mar. 2019.
[12] Gajender Kumar,Vinod Patidar,Prolay Biswas,Mukta Patel,Chaur Singh Rajput,Anita Venugopal,Aditi Sharma. "IOT enabled Intelligent featured imaging Bone Fractured Detection System." Journal of Intelligent Systems and Internet of Things, Vol. 9, No. 2, 2023 ,PP. 08-
[13] Han, C.-H., Kim, Y.-W., Kim, D. Y., Kim, S. H., Nenadic, Z., &Im, C.-H. (2019). Electroencephalography-based endogenous brain–computer interface for online communication with a completely locked-in patient. Journal of NeuroEngineering and Rehabilitation, 16(1). https://doi.org/10.1186/s12984-019-0493-0
[14] Venugopal, A., Sharma, A., Kumar, G. (2024). Applying Deep Hybrid Neural Network for Image Classification. In: Marriwala, N.K., Dhingra, S., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. MRCN 2023. Lecture Notes in Networks and Systems, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-97-0700-3_43
[15] Selvin Prem Kumar, S. et al. ‘An Efficient Hybrid Bert Model for Brain Tumor Classification’. 1 Jan. 2024 : 7241 – 7258.
[16] Abdulkader, S. N., Atia, A., & Mostafa, M.-S. M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16(2), 213–230. https://doi.org/10.1016/j.eij.2015.06.002
[17] Lin, C.-T., Lin, B.-S., Lin, F.-C., & Chang, C.-J. (2014). Brain Computer Interface-Based Smart Living Environmental Auto-Adjustment Control System in UPnP Home Networking. IEEE Systems Journal, 8(2), 363–370. https://doi.org/10.1109/JSYST.2012.2192756
[18] HintermüllerC., C. Kapeller, and GugerG. E. and C., “BCI Integration: Application Interfaces,” www.intechopen.com, Jun. 05, 2013.
[19] Scherer, R., Faller, J., Balderas, D., Friedrich, E. V. C., Pröll, M., Allison, B., & Müller-Putz, G. (2012). Brain–computer interfacing: more than the sum of its parts. Soft Computing, 17(2), 317–331. https://doi.org/10.1007/s00500-012-0895-4
[20] Wolpaw, J. R. (2013). Brain-computer interfaces. Handbook of Clinical Neurology, 110, 67–74. https://doi.org/10.1016/B978-0-444-52901-5.00006-X
[21] Scherer, R., Faller, J., Balderas, D., Friedrich, E. V. C., Pröll, M., Allison, B., & Müller-Putz, G. (2012). Brain–computer interfacing: more than the sum of its parts. Soft Computing, 17(2), 317–331. https://doi.org/10.1007/s00500-012-0895-4
[22] P. Jessy, “Analysis of EEG Signals for EEG-based Brain-Computer Interface,” Semantic Scholar, 2009.
[23] B., Jorge. , Mauricio, Kevin. , Marks, Adam. Fusion of Forensic Analysis of Mobile Devices: Integrating Multi-Criteria Decision Methods and Case Study Insights. Journal of Fusion: Practice and Applications, vol. 16, no. 2, 2024, pp. 32-42. DOI: https://doi.org/10.54216/FPA.160203
[24] Venkatasubramanian, G., Jayakumar, P. N., Nagendra, H. R., Nagaraja, D., Deeptha, R., &Gangadhar, B. N. (2008). Investigating paranormal phenomena: Functional brain imaging of telepathy. International Journal of Yoga, 1(2), 66–71. https://doi.org/10.4103/0973-6131.43543
[25] Tudor, M., Tudor, L., & Tudor, K. I. (2005). [Hans Berger (1873-1941)--the history of electroencephalography]. ActaMedicaCroatica: CasopisHravatskeAkademijeMedicinskihZnanosti, 59(4), 307–313. https://pubmed.ncbi.nlm.nih.gov/16334737/
[26] Chorlian, D. B., Porjesz, B., & Cohen, H. L. (1995). Measuring Electrical Activity of the Brain: ERP Mapping in Alcohol Research. Alcohol Health and Research World, 19(4), 315–320. https://pubmed.ncbi.nlm.nih.gov/31798076/