The Intersection of EEG Technology and Brain-Computer Interfaces in Medicine: A Review
- 1Anna University, Chennai, Department of ECE, K. Ramakrishnan college of engineering, Tamilnadu, IN
- 2Anna University, Chennai, Students, Department of ECE, K. Ramakrishnan college of Engineering, Tamilnadu, IN
- 3Anna University, Chennai, Students, Department of ECE, K. Ramakrishnan college of Engineering, Tamilnadu, IN
- 4Anna University, Chennai, Students, Department of ECE, K. Ramakrishnan college of Engineering, Tamilnadu, IN
- 5Anna University, Chennai, Students, Department of ECE, K. Ramakrishnan college of Engineering, Tamilnadu, IN
Conclusion
It
was decades later, however, before EEG BCIs became testable in a clinical
setting and stroke rehabilitation is where most of the results are now emerging.
The principle of motor imagery training, attempts at movement and sensorimotor
rhythm training is identical to what is seen in the laboratory: the brain
activity needs to be coupled with the appropriate feedback, and in controlled
experiments, it can be useful to certain patients in improving their upper limb
functions. There is little evidence thus far, but: the trials have been small,
the outcomes can vary depending on the patient and there has been no conclusive
evidence about the extent of the BCI's contribution to the outcome. It will all
depend on developing user-friendly devices, adaptive decoders and accurate
reporting of the trials. In such a scenario, BCIs could well become a regular
part of rehabilitation and speech therapy.
References
[1] S. Herculano-Houzel, “The human brain in
numbers: a linearly scaled-up primate brain,” Front. Hum. Neurosci., vol. 3,
art. 31, 2009.
[2] J. R. Wolpaw, N. Birbaumer, D. J. McFarland,
G. Pfurtscheller, and T. M. Vaughan, “Brain–computer interfaces for communication
and control,” Clin. Neurophysiol., vol. 113, no. 6, pp. 767–791, 2002.
[3] V. L. Feigin et al., “Global, regional, and
national burden of stroke and its risk factors, 1990–2019,” Lancet Neurol.,
vol. 20, no. 10, pp. 795–820, 2021.
[4] J. J. Daly and J. R. Wolpaw, “Brain–computer
interfaces in neurological rehabilitation,” Lancet Neurol., vol. 7, no. 11, pp.
1032–1043, 2008.
[5] P. L. Nunez and R. Srinivasan, Electric
Fields of the Brain: The Neurophysics of EEG, 2nd ed. New York, NY, USA: Oxford
Univ. Press, 2006.
[6] N. Naseer and K.-S. Hong, “fNIRS-based
brain–computer interfaces: a review,” Front. Hum. Neurosci., vol. 9, art. 3,
2015.
[7] L. R. Hochberg et al., “Reach and grasp by
people with tetraplegia using a neurally controlled robotic arm,” Nature, vol.
485, pp. 372–375, 2012.
[8] F. R. Willett et al., “A high-performance
speech neuroprosthesis,” Nature, vol. 620, pp. 1031–1036, 2023.
[9] L. A. Farwell and E. Donchin, “Talking off
the top of your head: toward a mental prosthesis utilizing event-related brain
potentials,” Electroencephalogr. Clin. Neurophysiol., vol. 70, no. 6, pp.
510–523, 1988.
[10] G. Pfurtscheller and F. H. Lopes da Silva,
“Event-related EEG/MEG synchronization and desynchronization: basic
principles,” Clin. Neurophysiol., vol. 110, no. 11, pp. 1842–1857, 1999.
[11] G. Pfurtscheller and C. Neuper, “Motor imagery
and direct brain–computer communication,” Proc. IEEE, vol. 89, no. 7, pp.
1123–1134, 2001.
[12] A. Delorme and S. Makeig, “EEGLAB: an open
source toolbox for analysis of single-trial EEG dynamics including independent
component analysis,” J. Neurosci. Methods, vol. 134, no. 1, pp. 9–21, 2004.
[13] C. A. E. Mullen et al., “Real-time neuroimaging
and cognitive monitoring using wearable dry EEG,” IEEE Trans. Biomed. Eng.,
vol. 62, no. 11, pp. 2553–2567, 2015.
[14] H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller,
“Optimal spatial filtering of single trial EEG during imagined hand movement,”
IEEE Trans. Rehabil. Eng., vol. 8, no. 4, pp. 441–446, 2000.
[15] B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe,
and K.-R. Müller, “Optimizing spatial filters for robust EEG single-trial
analysis,” IEEE Signal Process. Mag., vol. 25, no. 1, pp. 41–56, 2008.
[16] F. Lotte et al., “A review of classification
algorithms for EEG-based brain–computer interfaces: a 10 year update,” J.
Neural Eng., vol. 15, no. 3, art. 031005, 2018.
[17] V. J. Lawhern et al., “EEGNet: a compact
convolutional neural network for EEG-based brain–computer interfaces,” J.
Neural Eng., vol. 15, no. 5, art. 056013, 2018.
[18] A. Kübler et al., “Brain–computer
communication: unlocking the locked in,” Psychol. Bull., vol. 127, no. 3, pp.
358–375, 2001.
[19] S. R. Soekadar, N. Birbaumer, M. W. Slutzky,
and L. G. Cohen, “Brain–machine interfaces in neurorehabilitation of stroke,”
Neurobiol. Dis., vol. 83, pp. 172–179, 2015.
[20] F. Pichiorri et al., “Brain–computer interface
boosts motor imagery practice during stroke recovery,” Ann. Neurol., vol. 77,
no. 5, pp. 851–865, 2015.
[21] M. Ahn and S. C. Jun, “Performance variation in
motor imagery brain–computer interface: a brief review,” J. Neurosci. Methods,
vol. 243, pp. 103–110, 2015.
[22] A. Ramos-Murguialday et al., “Brain–machine
interface in chronic stroke rehabilitation: a controlled study,” Ann. Neurol.,
vol. 74, no. 1, pp. 100–108, 2013.
[23] E. Buch et al., “Think to move: a neuromagnetic
brain–computer interface (BCI) system for chronic stroke,” Stroke, vol. 39, no.
3, pp. 910–917, 2008.
[24] R. Sitaram et al., “Closed-loop brain training:
the science of neurofeedback,” Nat. Rev. Neurosci., vol. 18, no. 2, pp. 86–100,
2017.
[25] M. A. Cervera et al., “Brain–computer
interfaces for post-stroke motor rehabilitation: a meta-analysis,” Ann. Clin.
Transl. Neurol., vol. 5, no. 5, pp. 651–663, 2018.
[26] R. Mane, T. Chouhan, and C. Guan, “BCI for
stroke rehabilitation: motor and beyond,” J. Neural Eng., vol. 17, no. 4, art.
041001, 2020.
[27] A. R. Fugl-Meyer, L. Jääskö, I. Leyman, S.
Olsson, and S. Steglind, “The post-stroke hemiplegic patient. 1. A method for
evaluation of physical performance,” Scand. J. Rehabil. Med., vol. 7, no. 1,
pp. 13–31, 1975.
[28] S. J. Page, G. D. Fulk, and P. Boyne,
“Clinically important differences for the upper-extremity Fugl-Meyer scale in
people with minimal to moderate impairment due to chronic stroke,” Phys. Ther.,
vol. 92, no. 6, pp. 791–798, 2012.
[29] N. Birbaumer et al., “A spelling device for the
paralysed,” Nature, vol. 398, pp. 297–298, 1999.
[30] I. Lazarou, S. Nikolopoulos, P. C.
Petrantonakis, I. Kompatsiaris, and M. Tsolaki, “EEG-based brain–computer
interfaces for communication and rehabilitation of people with motor
impairment: a novel approach of the 21st century,” Front. Hum. Neurosci., vol.
12, art. 14, 2018.
[31] D. Cruse et al., “Bedside detection of
awareness in the vegetative state: a cohort study,” Lancet, vol. 378, no. 9809,
pp. 2088–2094, 2011.
[32] A. Craik, Y. He, and J. L. Contreras-Vidal,
“Deep learning for electroencephalogram (EEG) classification tasks: a review,”
J. Neural Eng., vol. 16, no. 3, art. 031001, 2019.
[33] U. Chaudhary, N. Birbaumer, and A.
Ramos-Murguialday, “Brain–computer interfaces for communication and
rehabilitation,” Nat. Rev. Neurol., vol. 12, no. 9, pp. 513–525, 2016.
[34] G. Pfurtscheller et al., “The hybrid BCI,”
Front. Neurosci., vol. 4, art. 30, 2010.
[35] B. Z. Allison et al., “A hybrid ERD/SSVEP BCI
for continuous simultaneous two-dimensional cursor control,” J. Neurosci.
Methods, vol. 209, no. 2, pp. 299–307, 2012.
Keywords: Brain-computer interface, electroencephalography, stroke rehabilitation, motor imagery, neuroplasticity, neurofeedback, assistive technology
Citation: Bala Kumar A*,Bala Kumar A ( 2026), The Intersection of EEG Technology and Brain-Computer Interfaces in Medicine: A Review. , 13(2): 1-6
Received: 10/06/2026; Accepted: 30/06/2026;
Published: 30/06/2026
Edited by:
Mr.ERES JOURNALS



