Brain-computer interface technology and its applications for patients with disorders of consciousness_Journal of Biomedical Engineering

Authors：

 PAN Jiahui ^1,2 , ZHANG Zhihang ¹ , ZHANG Yuanlin ¹ , WANG Fei ^1,2 , XIAO Jun ^2,3

1. School of Artificial Intelligence, South China Normal University, Guangzhou 510631, P. R. China;
2. Brain Computer Intelligence Research Center, Pazhou Lab, Guangzhou 510330, P. R. China;
3. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China;

Corresponding author：

PAN Jiahui, Email: panjiahui@m.scnu.edu.cn

Keywords：

Disorders of consciousness; Electroencephalogram-based brain-computer interface; Unresponsive wakefulness syndrome; Minimally conscious state

DOI：

10.7507/1001-5515.202410061

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.

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3.	Weaver J A, Cogan A M, Kozlowski A J, et al. Interpreting change in disorders of consciousness using the coma recovery scale-revised. Journal of Neurotrauma, 2024, 41(15-16): e1996-e2008.
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5.	Bodien Y G, Barra A, Temkin N R, et al. Diagnosing level of consciousness: the limits of the Glasgow coma scale total score. Journal of Neurotrauma, 2021, 38(23): 3295-3305.
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7.	Porcaro C, Marino M, Carozzo S, et al. Fractal dimension feature as a signature of severity in disorders of consciousness: an EEG study. International Journal of Neural Systems, 2022, 32(7): 2250031.
8.	Bodien Y G, Allanson J, Cardone P, et al. Cognitive motor dissociation in disorders of consciousness. New England Journal of Medicine, 2024, 391(7): 598-608.
9.	He Q, He J, Yang Y, et al. Brain-computer interfaces in disorders of consciousness. Neuroscience Bulletin, 2023, 39(2): 348-352.
10.	Rossi Sebastiano D, Varotto G, Sattin D, et al. EEG assessment in patients with disorders of consciousness: aims, advantages, limits, and pitfalls. Frontiers in Neurology, 2021, 12: 649849.
11.	Zheng R Z, Qi Z X, Wang Z, et al. Clinical decision on disorders of consciousness after acquired brain injury: stepping forward. Neuroscience Bulletin, 2023, 39(1): 138-162.
12.	Pruvost-Robieux E, Marchi A, Martinelli I, et al. Evoked and event-related potentials as biomarkers of consciousness state and recovery. Journal of Clinical Neurophysiology, 2022, 39(1): 22-31.
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15.	Lulé D, Noirhomme Q, Kleih S C, et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clinical Neurophysiology, 2013, 124(1): 101-106.
16.	Coyle D, Stow J, McCreadie K, et al. Sensorimotor modulation assessment and brain-computer interface training in disorders of consciousness. Archives of Physical Medicine and Rehabilitation, 2015, 96(3): S62-S70.
17.	Wang F, He Y, Pan J, et al. A novel audiovisual brain-computer interface and its application in awareness detection. Scientific Reports, 2015, 5: 9962.
18.	Pan J, Wang L, Huang H, et al. A hybrid brain–computer interface combining P300 potentials and emotion patterns for detecting awareness in patients with disorders of consciousness. IEEE Transactions on Cognitive and Developmental Systems, 2023, 15(3): 1386-1395.
19.	Huang J, Qiu L, Lin Q, et al. Hybrid asynchronous brain–computer interface for yes/no communication in patients with disorders of consciousness. Journal of Neural Engineering, 2021, 18(5): 056001.
20.	Zhang L, Zhang R, Guo Y, et al. Assessing residual motor function in patients with disorders of consciousness by brain network properties of task-state EEG. Cognitive Neurodynamics, 2022, 16(3): 609-620.
21.	Pan J, Liang R, He Z, et al. ST-SCGNN: a spatio-temporal self-constructing graph neural network for cross-subject EEG-based emotion recognition and consciousness detection. IEEE Journal of Biomedical and Health Informatics, 2024, 28(2): 777-788.
22.	Pan J, Yu Y, Wu J, et al. Deep neural networks for automatic sleep stage classification and consciousness assessment in patients with disorder of consciousness. IEEE Transactions on Cognitive and Developmental Systems, 2024, 16(4): 1589-1603.
23.	Pan J, Chen Y, Xiao Q, et al. Assessing consciousness in patients with disorders of consciousness using a musical stimulation paradigm and verifiable criteria. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 2971-2982.
24.	Spataro R, Xu Y, Xu R, et al. How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: a comparative study. Frontiers in Neuroscience, 2022, 16: 959339.
25.	Xiao J, He Y, Yu T, et al. Toward assessment of sound localization in disorders of consciousness using a hybrid audiovisual brain-computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1422-1432.
26.	Annen J, Mertel I, Xu R, et al. Auditory and somatosensory P3 are complementary for the assessment of patients with disorders of consciousness. Brain Sciences, 2020, 10(10): 748.
27.	Yi Z, Pan J, Chen Z, et al. A hybrid BCI integrating EEG and eye-tracking for assisting clinical communication in patients with disorders of consciousness. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 2759-2771.
28.	Zhao S, Cao Y, Yang W, et al. DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness. Journal of Neural Engineering, 2024, 21(5): 056021.
29.	Wu H, Xiao J, Xue C, et al. A portable and wearable visual brain-computer interface for assessing patients with disorders of consciousness//2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC), Wuhan: IEEE, 2023: 1-5.
30.	Estraneo A, Magliacano A, Fiorenza S, et al. Risk factors for 2-year mortality in patients with prolonged disorders of consciousness: an international multicentre study. European Journal of Neurology, 2022, 29(2): 390-399.
31.	Young M J, Edlow B L. The quest for covert consciousness: bringing neuroethics to the bedside. Neurology, 2021, 96(19): 893-896.
32.	Edlow B L, Claassen J, Schiff N D, et al. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nature Reviews Neurology, 2021, 17(3): 135-156.
33.	Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
34.	Li J, Huang B, Wang F, et al. A potential prognosis Indicator based on P300 brain–computer interface for patients with disorder of consciousness. Brain Sciences, 2022, 12(11): 1556.
35.	Murovec N, Heilinger A, Xu R, et al. Effects of a vibro-tactile P300 based brain-computer interface on the coma recovery scale-revised in patients with disorders of consciousness. Frontiers in Neuroscience, 2020, 14: 294.
36.	Wang Y, Liu W, Wang Y, et al. Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: a resting-state EEG study. Computers in Biology and Medicine, 2024, 170: 108084.
37.	Galiotta V, Quattrociocchi I, D'Ippolito M, et al. EEG-based brain-computer interfaces for people with disorders of consciousness: features and applications. A systematic review. Frontiers in Human Neuroscience, 2022, 16: 1040816.
38.	Chatelle C, Spencer C A, Cash S S, et al. Feasibility of an EEG-based brain-computer interface in the intensive care unit. Clinical Neurophysiology, 2018, 129(8): 1519-1525.
39.	Young M J, Bodien Y G, Giacino J T, et al. The neuroethics of disorders of consciousness: a brief history of evolving ideas. Brain, 2021, 144(11): 3291-3310.

1. Golden K, Bodien Y G, Giacino J T. Disorders of consciousness: classification and taxonomy. Physical Medicine and Rehabilitation Clinics, 2024, 35(1): 15-33.
2. Ferré F, Heine L, Naboulsi E, et al. Self-processing in coma, unresponsive wakefulness syndrome and minimally conscious state. Frontiers in Human Neuroscience, 2023, 17: 1145253.
3. Weaver J A, Cogan A M, Kozlowski A J, et al. Interpreting change in disorders of consciousness using the coma recovery scale-revised. Journal of Neurotrauma, 2024, 41(15-16): e1996-e2008.
4. Woodward M R, Wells C L, Arnold S, et al. Behavioral assessment with the coma recovery scale-revised is safe and feasible in critically Ill patients with disorders of consciousness. Critical Care Explorations, 2024, 6(7): e1101.
5. Bodien Y G, Barra A, Temkin N R, et al. Diagnosing level of consciousness: the limits of the Glasgow coma scale total score. Journal of Neurotrauma, 2021, 38(23): 3295-3305.
6. Porcaro C, Nemirovsky I E, Riganello F, et al. Diagnostic developments in differentiating unresponsive wakefulness syndrome and the minimally conscious state. Frontiers in Neurology, 2022, 12: 778951.
7. Porcaro C, Marino M, Carozzo S, et al. Fractal dimension feature as a signature of severity in disorders of consciousness: an EEG study. International Journal of Neural Systems, 2022, 32(7): 2250031.
8. Bodien Y G, Allanson J, Cardone P, et al. Cognitive motor dissociation in disorders of consciousness. New England Journal of Medicine, 2024, 391(7): 598-608.
9. He Q, He J, Yang Y, et al. Brain-computer interfaces in disorders of consciousness. Neuroscience Bulletin, 2023, 39(2): 348-352.
10. Rossi Sebastiano D, Varotto G, Sattin D, et al. EEG assessment in patients with disorders of consciousness: aims, advantages, limits, and pitfalls. Frontiers in Neurology, 2021, 12: 649849.
11. Zheng R Z, Qi Z X, Wang Z, et al. Clinical decision on disorders of consciousness after acquired brain injury: stepping forward. Neuroscience Bulletin, 2023, 39(1): 138-162.
12. Pruvost-Robieux E, Marchi A, Martinelli I, et al. Evoked and event-related potentials as biomarkers of consciousness state and recovery. Journal of Clinical Neurophysiology, 2022, 39(1): 22-31.
13. Zhang G, Cui Y, Zhang Y, et al. Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level. NeuroImage, 2021, 237: 118166.
14. Amaunam I, Schneider C, da Silva M L, et al. A discussion of statistical criteria for assessing awareness with SMR BCI after brain injury//2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Honolulu: IEEE, 2023: 3709-3714.
15. Lulé D, Noirhomme Q, Kleih S C, et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clinical Neurophysiology, 2013, 124(1): 101-106.
16. Coyle D, Stow J, McCreadie K, et al. Sensorimotor modulation assessment and brain-computer interface training in disorders of consciousness. Archives of Physical Medicine and Rehabilitation, 2015, 96(3): S62-S70.
17. Wang F, He Y, Pan J, et al. A novel audiovisual brain-computer interface and its application in awareness detection. Scientific Reports, 2015, 5: 9962.
18. Pan J, Wang L, Huang H, et al. A hybrid brain–computer interface combining P300 potentials and emotion patterns for detecting awareness in patients with disorders of consciousness. IEEE Transactions on Cognitive and Developmental Systems, 2023, 15(3): 1386-1395.
19. Huang J, Qiu L, Lin Q, et al. Hybrid asynchronous brain–computer interface for yes/no communication in patients with disorders of consciousness. Journal of Neural Engineering, 2021, 18(5): 056001.
20. Zhang L, Zhang R, Guo Y, et al. Assessing residual motor function in patients with disorders of consciousness by brain network properties of task-state EEG. Cognitive Neurodynamics, 2022, 16(3): 609-620.
21. Pan J, Liang R, He Z, et al. ST-SCGNN: a spatio-temporal self-constructing graph neural network for cross-subject EEG-based emotion recognition and consciousness detection. IEEE Journal of Biomedical and Health Informatics, 2024, 28(2): 777-788.
22. Pan J, Yu Y, Wu J, et al. Deep neural networks for automatic sleep stage classification and consciousness assessment in patients with disorder of consciousness. IEEE Transactions on Cognitive and Developmental Systems, 2024, 16(4): 1589-1603.
23. Pan J, Chen Y, Xiao Q, et al. Assessing consciousness in patients with disorders of consciousness using a musical stimulation paradigm and verifiable criteria. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 2971-2982.
24. Spataro R, Xu Y, Xu R, et al. How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: a comparative study. Frontiers in Neuroscience, 2022, 16: 959339.
25. Xiao J, He Y, Yu T, et al. Toward assessment of sound localization in disorders of consciousness using a hybrid audiovisual brain-computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1422-1432.
26. Annen J, Mertel I, Xu R, et al. Auditory and somatosensory P3 are complementary for the assessment of patients with disorders of consciousness. Brain Sciences, 2020, 10(10): 748.
27. Yi Z, Pan J, Chen Z, et al. A hybrid BCI integrating EEG and eye-tracking for assisting clinical communication in patients with disorders of consciousness. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 2759-2771.
28. Zhao S, Cao Y, Yang W, et al. DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness. Journal of Neural Engineering, 2024, 21(5): 056021.
29. Wu H, Xiao J, Xue C, et al. A portable and wearable visual brain-computer interface for assessing patients with disorders of consciousness//2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC), Wuhan: IEEE, 2023: 1-5.
30. Estraneo A, Magliacano A, Fiorenza S, et al. Risk factors for 2-year mortality in patients with prolonged disorders of consciousness: an international multicentre study. European Journal of Neurology, 2022, 29(2): 390-399.
31. Young M J, Edlow B L. The quest for covert consciousness: bringing neuroethics to the bedside. Neurology, 2021, 96(19): 893-896.
32. Edlow B L, Claassen J, Schiff N D, et al. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nature Reviews Neurology, 2021, 17(3): 135-156.
33. Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
34. Li J, Huang B, Wang F, et al. A potential prognosis Indicator based on P300 brain–computer interface for patients with disorder of consciousness. Brain Sciences, 2022, 12(11): 1556.
35. Murovec N, Heilinger A, Xu R, et al. Effects of a vibro-tactile P300 based brain-computer interface on the coma recovery scale-revised in patients with disorders of consciousness. Frontiers in Neuroscience, 2020, 14: 294.
36. Wang Y, Liu W, Wang Y, et al. Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: a resting-state EEG study. Computers in Biology and Medicine, 2024, 170: 108084.
37. Galiotta V, Quattrociocchi I, D'Ippolito M, et al. EEG-based brain-computer interfaces for people with disorders of consciousness: features and applications. A systematic review. Frontiers in Human Neuroscience, 2022, 16: 1040816.
38. Chatelle C, Spencer C A, Cash S S, et al. Feasibility of an EEG-based brain-computer interface in the intensive care unit. Clinical Neurophysiology, 2018, 129(8): 1519-1525.
39. Young M J, Bodien Y G, Giacino J T, et al. The neuroethics of disorders of consciousness: a brief history of evolving ideas. Brain, 2021, 144(11): 3291-3310.

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