Neuroinformatics and Its Impact on the Future of Brain-Computer Interface Technology

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2024 | Volume : 13 | Issue : 03 | Page : 9 18
    By

    Aditi Arvi,

  1. Student, Faculty of Biotechnology, University of Allahabad, Prayagraj, Uttar Pradesh, India

Abstract

Neuroinformatics, a multidisciplinary field combining neuroscience, information technology, and data science, plays a crucial role in advancing brain-computer interface (BCI) technology. By leveraging large-scale neural data, machine learning algorithms, and computational models, neuroinformatics enhances our understanding of brain function and improves the design and development of BCIs. The integration of neuroinformatics into BCI systems offers new possibilities for interpreting complex brain signals, facilitating real-time communication between the brain and external devices, and enabling new treatments for neurological disorders. Recent developments in neuroinformatics have significantly improved signal processing techniques, data storage, and analysis methods, allowing for better accuracy and efficiency in BCI systems. These advancements help decode brain activity in ways that were previously not possible, leading to more effective BCIs that can assist individuals with motor disabilities, provide communication solutions for patients with neurological impairments, and even allow for direct brain control of prosthetics. Furthermore, neuroinformatics provides a platform for cross-disciplinary collaboration, where researchers from diverse fields can share data and insights, fostering innovation in BCI technologies. The future of BCIs is highly dependent on continued advancements in neuroinformatics. The combination of cutting-edge computational models, artificial intelligence, and neuroimaging techniques promises to enhance BCI capabilities, making them more accessible, adaptable, and precise. As neuroinformatics continues to evolve, it holds the potential to revolutionize the interaction between humans and machines, opening up new possibilities for medical, cognitive, and even ethical applications. This paper explores the relationship between neuroinformatics and BCI technology, examining current trends, challenges, and future prospects, highlighting how this synergy is poised to shape the future of brain-machine interactions.

Keywords: Neuroinformatics, brain-computer interface, neurological disorders, signal processing techniques, brain activity

[This article belongs to Research & Reviews : Journal of Computational Biology ]

How to cite this article:
Aditi Arvi. Neuroinformatics and Its Impact on the Future of Brain-Computer Interface Technology. Research & Reviews : Journal of Computational Biology. 2024; 13(03):9-18.
How to cite this URL:
Aditi Arvi. Neuroinformatics and Its Impact on the Future of Brain-Computer Interface Technology. Research & Reviews : Journal of Computational Biology. 2024; 13(03):9-18. Available from: https://journals.stmjournals.com/rrjocb/article=2024/view=190340


References

1. Lebedev MA, Nicolelis MA. Brain–machine interfaces: Past, present and future. Trends Neurosci. 2006;29(9):536–546. doi: 10.1016/j.tins.2006.07.004.
2. He H, Wu D. Transfer learning for brain–computer interfaces: A Euclidean space data alignment approach. IEEE Trans Biomed Eng. 2019;67(2):399–410. doi: 10.1109/TBME.2019.2913914.
3. Lebedev MA, Nicolelis MA. Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation. Physiol Rev. 2017;97(2):767–837. doi: 10.1152/physrev.00027.2016.
4. Carmena JM. Advances in neuroprosthetic learning and control. PLoS Biol. 2013;11(5):e1001561. doi: 10.1371/journal.pbio.1001561.
5. Azab AM, Toth J, Mihaylova LS, Arvaneh M. A review on transfer learning approaches in brain–computer interface. In: Signal Processing and Machine Learning for Brain-machine Interfaces. IET; 2018. pp. 81–98. doi: 10.1049/PBCE114E_ch5.
6. Lebedev M. Brain-machine interfaces: An overview. Transl Neurosci. 2014;5(1):99–110. doi: 10.2478/s13380-014-0212-z.
7. Jayaram V, Alamgir M, Altun Y, Scholkopf B, Grosse-Wentrup M. Transfer learning in brain-computer interfaces. IEEE Comput Intell Mag. 2016;11(1):20–31. doi: 10.48550/arXiv.1512.00296.
8. Peterson V, Vissani M, Luo S, Rabbani Q, Crone NE, Bush A, et al. A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings. bioRxiv [Preprint]. 2023:2023.04.05.535577. doi: 10.1101/2023.04.05.535577.
9. Arvaneh M, Guan C, Ang KK, Quek C. EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface. Neural Comput. 2013;25(8):2146–2171. doi: 10.1162/NECO_a_00474.
10. Wu D, Xu Y, Lu BL. Transfer learning for EEG-based brain–computer interfaces: A review of progress made since 2016. IEEE Trans Cogn Dev Syst. 2020;14(1):4–19. doi: 10.1109/TCDS.2020.3007453.
11. Zanini P, Congedo M, Jutten C, Said S, Berthoumieu Y. Transfer learning: A Riemannian geometry framework with applications to brain–computer interfaces. IEEE Trans Biomed Eng. 2017;65(5):1107–1116. doi: 10.1109/TBME.2017.2742541.
12. Gu X, Cao Z, Jolfaei A, Xu P, Wu D, Jung TP, et al. EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications. IEEE/ACM Trans Comput Biol Bioinform. 2021;18(5):1645–1666. doi: 10.1109/TCBB.2021.3052811.
13. Huang H, Xie Q, Pan J, He Y, Wen Z, Yu R, et al. An EEG-based brain computer interface for emotion recognition and its application in patients with disorder of consciousness. IEEE Trans Affect Comput. 2019;12(4):832–842. doi: . 10.1109/TAFFC.2019.2901456.
14. He H, Wu D. Transfer learning for brain–computer interfaces: A Euclidean space data alignment approach. IEEE Trans Biomed Eng. 2019;67(2):399–410. doi: 10.1109/TBME.2019.2913914.
15. Värbu K, Muhammad N, Muhammad Y. Past, present, and future of EEG-based BCI applications. Sensors (Basel). 2022;22(9):3331. doi: 10.3390/s22093331.
16. Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng. 2007;4(2):R1–R13. doi: 10.1088/1741-2560/4/2/R01
17. Radhakrishnan P, Ahmed AN, Kalaiarasi K, Giridhar K, Thenappan S. EEG-based brain-computer interfaces using gazelle optimization algorithm with deep learning for motor-imagery classification. Fusion Pract Appl. 2024;16(1):37–51. doi: 10.54216/FPA.160103.
18. Shenoy KV, Carmena JM. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron. 2014;84(4):665–680. doi: 10.1016/j.neuron.2014.08.038.
19. Hochberg LR, Donoghue JP. Sensors for brain-computer interfaces. IEEE Eng Med Biol Mag. 2006;25(5):32–38. doi: 10.1109/memb.2006.1705745
20. Meng J, Yao L, Sheng X, Zhang D, Zhu X. Simultaneously optimiz+ing spatial spectral features based on mutual information for EEG classification. IEEE Trans Biomed Eng. 2015;62(1):227–240. doi: 10.1109/TBME.2014.2345458.
21. Cai Y, She Q, Ji J, Ma Y, Zhang J, Zhang Y. Motor imagery EEG decoding using manifold embedded transfer learning. J Neurosci Methods. 2022:370:109489. doi: 10.1016/j.jneumeth.2022.109489.
22. Bucy PC. Electroencephalography’s proper role: Guest editorial. JAMA. 1956;160(14):1232–1233. doi: 10.1001/jama.1956.02960490046012.
23. Kellaway P, Conn HF. Electroencephalography in the diagnosis and management of the epilepsies. Medical Clinics of North America. 1958;42(2):439–460.
24. Cobb W, London GB, Gastaut H, Hess Jr R, Jung R, Magnus O, et al. Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr Clin Neurophysiol. 1958;10(2):370–375.
25. Dawson GD, Walter WG. Recommendations for the design and performance of electroencephalographic apparatus. J Neurol Neurosurg Psychiatry. 1945;8(3-4):61–64. doi: 10.1136/jnnp.8.3-4.61.

Regular Issue Subscription Original Research
Volume 13
Issue 03
Received 26/11/2024
Accepted 02/12/2024
Published 19/12/2024
306

Login


My IP

PlumX Metrics