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The evolution and application of mechanical instruments in brain-computer interface technology
- 1 University of Manchester
* Author to whom correspondence should be addressed.
Abstract
Brain-Computer Interfaces (BCIs) are innovative systems that facilitate direct communication between the human brain and external devices. Leveraging advances in neuroscience and engineering, BCIs can decode neural activity, allowing users to control computers, prosthetics, or even communicate thoughts without the need for peripheral motor activity. While primarily developed for assisting individuals with motor or communication disabilities, the potential applications span from gaming to advanced robotics. Despite rapid progress, challenges remain in achieving high-resolution decoding and ensuring long-term stability. As the field advances, ethical considerations about privacy, security, and human augmentation also emerge. This paper aims to provide an in-depth exploration of the field of BCI technology. The first part illustrates the remarkable evolution undergone by BCI, from electroencephalography (EEG) to functional magnetic resonance imaging (fMRI). The next part mainly describes the principle of BCI and the devices that are used for processing the interface. The following part highlights the challenge faced by BCI and how ethics and security should be deliberated.
Keywords
Brain-Computer Interface Technology, Mechanical Instruments, Communication, Channel
[1]. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791.
[2]. Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the brain: The neurophysics of EEG. Oxford Univesity press. New York
[3]. Jasper, H. H. (1958). The ten-twenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology, 10, 371-375.
[4]. Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1-11.
[5]. Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique. Cambridge, MA
[6]. Widmann, A., Schröger, E., & Maess, B. (2015). Digital filter design for electrophysiological data – a practical approach. Journal of Neuroscience Methods, 250, 34-46.
[7]. Sanei, S., & Chambers, J. A. (2007). EEG signal processing. Chichester, UK: John Wiley & Sons.
[8]. Naseer, N., & Hong, K. S. (2015). fNIRS-based brain-computer interfaces: a review. Frontiers in Human Neuroscience, 9, 3.
[9]. Obrig, H., & Villringer, A. (2003). Beyond the visible—Imaging the human brain with light. Journal of Cerebral Blood Flow & Metabolism, 23(1), 1-18.
[10]. Boas, D. A., Elwell, C. E., Ferrari, M., & Taga, G. (2014). Twenty years of functional near-infrared spectroscopy: Introduction for the special issue. NeuroImage, 85, 1-5.
[11]. Duncan, A., Meek, J. H., Clemence, M., Elwell, C. E., Tyszczuk, L., Cope, M., & Delpy, D. T. (1996). Optical path length measurements on adult head, calf and forearm and the head of the newborn infant using phase resolved optical spectroscopy. Physics in Medicine & Biology, 40(2), 295
[12]. Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Pavia, J. M., Wolf, U., & Wolf, M. (2014). A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage, 85, 6-27.
[13]. Gervain, J., Mehler, J., Werker, J. F., Nelson, C. A., Csibra, G., Lloyd-Fox, S., ... & Aslin, R. N. (2011). Near-infrared spectroscopy: A report from the McDonnell infant methodology consortium. Developmental Cognitive Neuroscience, 1(1), 22-46.
[14]. Schalk, G., & Leuthardt, E. C. (2011). Brain–computer interfaces using electrocorticographic signals. IEEE Reviews in Biomedical Engineering, 4, 140-154.
[15]. Bauer, P. R., Reitsma, J. B., Houweling, B. M., Ferrier, C. H., & Ramsey, N. F. (2017). Can fMRI safely replace the Wada test for preoperative assessment of language lateralisation? A meta-analysis and systematic review. Journal of Neurology, Neurosurgery & Psychiatry, 88(11), 958-966.
[16]. Rutishauser, U., Ross, I. B., Mamelak, A. N., & Schuman, E. M. (2010). Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature, 464(7290), 903-907
[17]. Nicolelis, M. A., & Lebedev, M. A. (2009). Principles of neural ensemble physiology underlying the operation of the brain–machine interfaces. Nature Reviews Neuroscience, 10(7), 530-540.
[18]. He, B., & Wu, D. (2014). Transfer of brain-computer interface technology into clinical applications. Brain-Computer Interfaces, 1(4), 231-240.
[19]. Lebedev, M. A., & Nicolelis, M. A. (2006). Brain–machine interfaces: Past, present and future. Trends in Neurosciences, 29(9), 536-546.
[20]. Yuste, R., Goering, S., Bi, G., Carmena, J. M., Carter, A., Fins, J. J., ... & Rommelfanger, K. S. (2017). Four ethical priorities for neurotechnologies and AI. Nature News, 551(7679), 159.
[21]. Kellmeyer, P., Cochrane, T., Müller, O., Mitchell, C., Ball, T., Fins, J. J., & Biller-Andorno, N. (2016). The effects of closed-loop medical devices on the autonomy and accountability of persons and systems. Cambridge Quarterly of Healthcare Ethics, 25(4), 623-633.
Cite this article
Mo,S. (2023). The evolution and application of mechanical instruments in brain-computer interface technology. Theoretical and Natural Science,16,97-101.
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