Brain–Computer Interfaces for Awareness Detection, Auxiliary Diagnosis, Prognosis, and Rehabilitation in Patients with Disorders of Consciousness

Jiahui Pan; Jun Xiao; Jing Wang; Fei Wang; Jingcong Li; Lina Qiu; Haibo Di; Yuanqing Li

doi:10.1055/a-1900-7261

Seminars in Neurology, Table of Contents

Semin Neurol 2022; 42(03): 363-374
DOI: 10.1055/a-1900-7261

Review Article

Brain–Computer Interfaces for Awareness Detection, Auxiliary Diagnosis, Prognosis, and Rehabilitation in Patients with Disorders of Consciousness

Jiahui Pan

¹Pazhou Lab, Guangzhou, People's Republic of China

²School of Software, South China Normal University, Guangzhou, People's Republic of China

,

Jun Xiao

¹Pazhou Lab, Guangzhou, People's Republic of China

³School of Automation Science and Engineering, South China University of Technology, Guangzhou, People's Republic of China

,

Jing Wang

⁴International Unresponsive Wakefulness Syndrome and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, People's Republic of China

,

Fei Wang

¹Pazhou Lab, Guangzhou, People's Republic of China

²School of Software, South China Normal University, Guangzhou, People's Republic of China

,

Jingcong Li

¹Pazhou Lab, Guangzhou, People's Republic of China

²School of Software, South China Normal University, Guangzhou, People's Republic of China

,

Lina Qiu

²School of Software, South China Normal University, Guangzhou, People's Republic of China

,

Haibo Di

⁴International Unresponsive Wakefulness Syndrome and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, People's Republic of China

,

Yuanqing Li

¹Pazhou Lab, Guangzhou, People's Republic of China

³School of Automation Science and Engineering, South China University of Technology, Guangzhou, People's Republic of China

› Author Affiliations

Abstract

In recent years, neuroimaging studies have remarkably demonstrated the presence of cognitive motor dissociation in patients with disorders of consciousness (DoC). These findings accelerated the development of brain–computer interfaces (BCIs) as clinical tools for behaviorally unresponsive patients. This article reviews the recent progress of BCIs in patients with DoC and discusses the open challenges. In view of the practical application of BCIs in patients with DoC, four aspects of the relevant literature are introduced: consciousness detection, auxiliary diagnosis, prognosis, and rehabilitation. For each aspect, the paradigm design, brain signal processing methods, and experimental results of representative BCI systems are analyzed. Furthermore, this article provides guidance for BCI design for patients with DoC and discusses practical challenges for future research.

Keywords

disorders of consciousness - brain–computer interfaces - EEG-based BCIs - unresponsive wakefulness syndrome - minimally conscious state

Full Text

References

References
1 Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil 2004; 85 (12) 2020-2029
2 Giacino JT, Ashwal S, Childs N. et al. The minimally conscious state: definition and diagnostic criteria. Neurology 2002; 58 (03) 349-353
3 Gosseries O, Zasler ND, Laureys S. Recent advances in disorders of consciousness: focus on the diagnosis. Brain Inj 2014; 28 (09) 1141-1150
4 Johnson LSM, Christos L. The sources of uncertainty in disorders of consciousness. AJOB Neurosci 2018; 9: 76-82
5 Schnakers C, Vanhaudenhuyse A, Giacino J. et al. Diagnostic accuracy of the vegetative and minimally conscious state: clinical consensus versus standardized neurobehavioral assessment. BMC Neurol 2009; 9: 35
6 Wang J, Hu X, Hu Z, Sun Z, Laureys S, Di H. The misdiagnosis of prolonged disorders of consciousness by a clinical consensus compared with repeated coma-recovery scale-revised assessment. BMC Neurol 2020; 20 (01) 343
7 Li Y, Pan J, Long J. et al. Multimodal BCIs: target detection, multidimensional control, and awareness evaluation in patients with disorder of consciousness. Proc IEEE 2016; 104: 332-352
8 Gibson RM, Owen AM, Cruse D. Brain-computer interfaces for patients with disorders of consciousness. Prog Brain Res 2016; 228: 241-291
9 Schiff ND. Cognitive motor dissociation following severe brain injuries. JAMA Neurol 2015; 72 (12) 1413-1415
10 Boly M, Laureys S. Functional ‘unlocking’: bedside detection of covert awareness after severe brain damage. Brain 2018; 141 (05) 1239-1241
11 Rossi Sebastiano D, Varotto G, Sattin D, Franceschetti S. EEG assessment in patients with disorders of consciousness: aims, advantages, limits, and pitfalls. Front Neurol 2021; 12: 649849
12 Kübler A, Birbaumer N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?. Clin Neurophysiol 2008; 119 (11) 2658-2666
13 Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002; 113 (06) 767-791
14 Guger C, Daban S, Sellers E. et al. How many people are able to control a P300-based brain-computer interface (BCI)?. Neurosci Lett 2009; 462 (01) 94-98
15 Müller-Putz GR, Scherer R, Neuper C, Pfurtscheller G. Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?. IEEE Trans Neural Syst Rehabil Eng 2006; 14 (01) 30-37
16 Edelman BJ, Meng J, Gulachek N, Cline CC, He B. Exploring cognitive flexibility with a noninvasive BCI using simultaneous steady-state visual evoked potentials and sensorimotor rhythms. IEEE Trans Neural Syst Rehabil Eng 2018; 26 (05) 936-947
17 Lulé D, Noirhomme Q, Kleih SC. et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin Neurophysiol 2013; 124 (01) 101-106
18 Pan J, Xie Q, He Y. et al. Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface. J Neural Eng 2014; 11 (05) 056007
19 Li Y, Pan J, He Y. et al. Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system. BMC Neurol 2015; 15: 259
20 Wang F, He Y, Pan J. et al. A novel audiovisual brain-computer interface and its application in awareness detection. Sci Rep 2015; 5: 9962
21 Coyle D, Stow J, McCreadie K, McElligott J, Carroll Á. Sensorimotor modulation assessment and brain-computer interface training in disorders of consciousness. Arch Phys Med Rehabil 2015; 96 (3, Suppl): S62-S70
22 Xie Q, Pan J, Chen Y. et al. A gaze-independent audiovisual brain-computer Interface for detecting awareness of patients with disorders of consciousness. BMC Neurol 2018; 18 (01) 144
23 Curley WH, Forgacs PB, Voss HU, Conte MM, Schiff ND. Characterization of EEG signals revealing covert cognition in the injured brain. Brain 2018; 141 (05) 1404-1421
24 Guger C, Spataro R, Pellas F. et al. Assessing command-following and communication with vibro-tactile P300 brain-computer interface tools in patients with unresponsive wakefulness syndrome. Front Neurosci 2018; 12: 423
25 Huang J, Qiu L, Lin Q. et al. Hybrid asynchronous brain-computer interface for yes/no communication in patients with disorders of consciousness. J Neural Eng 2021; 18 (05) 18
26 Huang H, Xie Q, Pan J. et al. An EEG-based brain computer interface for emotion recognition and its application in patients with disorder of consciousness. . IEEE T Affect Comput 2019:1–1
27 Li Z, Zhang S, Pan J. Advances in hybrid brain-computer interfaces: principles, design, and applications. Comput Intell Neurosci 2019; 2019: 3807670
28 Chennu S, Finoia P, Kamau E. et al. Dissociable endogenous and exogenous attention in disorders of consciousness. Neuroimage Clin 2013; 3: 450-461
29 Xiao J, Xie Q, He Y. et al. An auditory BCI system for assisting CRS-R behavioral assessment in patients with disorders of consciousness. Sci Rep 2016; 6: 32917
30 Wang F, He Y, Qu J. et al. Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness. J Neural Eng 2017; 14 (04) 046024
31 Xiao J, Pan J, He Y. et al. Visual fixation assessment in patients with disorders of consciousness based on brain-computer interface. Neurosci Bull 2018; 34 (04) 679-690
32 Xiao J, Xie Q, Lin Q, Yu T, Yu R, Li Y. Assessment of visual pursuit in patients with disorders of consciousness based on a brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 2018; 26 (06) 1141-1151
33 Wang F, He Y, Qu J. et al. A brain–computer interface based on three-dimensional stereo stimuli for assisting clinical object recognition assessment in patients with disorders of consciousness. IEEE Trans Neural Syst Rehabil Eng 2019; 27 (03) 507-513
34 Annen J, Mertel I, Xu R. et al. Auditory and somatosensory P3 are complementary for the assessment of patients with disorders of consciousness. Brain Sci 2020; 10 (10) 748
35 Wijnen VJ, Eilander HJ, de Gelder B, van Boxtel GJ. Visual processing during recovery from vegetative state to consciousness: comparing behavioral indices to brain responses. Neurophysiol Clin 2014; 44 (05) 457-469
36 Giacino JT, Kalmar K. Diagnostic and prognostic guidelines for the vegetative and minimally conscious states. Neuropsychol Rehabil 2005; 15 (3-4): 166-174
37 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. Eur J Neurol 2022; 29 (02) 390-399
38 Young MJ, Edlow BL. The quest for covert consciousness: bringing neuroethics to the bedside. Neurology 2021; 96 (19) 893-896
39 Peterson A, Owen AM, Karlawish J. Translating the discovery of covert consciousness into clinical practice. JAMA Neurol 2020; 77 (05) 541-542
40 Owen AM. Improving diagnosis and prognosis in disorders of consciousness. Brain 2020; 143 (04) 1050-1053
41 Risetti M, Formisano R, Toppi J. et al. On ERPs detection in disorders of consciousness rehabilitation. Front Hum Neurosci 2013; 7: 775
42 Claassen J, Doyle K, Matory A. et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med 2019; 380 (26) 2497-2505
43 Pan J, Xie Q, Qin P. et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain 2020; 143 (04) 1177-1189
44 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. Front Neurosci 2020; 14: 294
45 Cruse D, Chennu S, Chatelle C. et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet 2011; 378 (9809): 2088-2094
46 Pokorny C, Klobassa DS, Pichler G. et al. The auditory P300-based single-switch brain-computer interface: paradigm transition from healthy subjects to minimally conscious patients. Artif Intell Med 2013; 59 (02) 81-90
47 Henriques J, Gabriel D, Grigoryeva L. et al. Protocol design challenges in the detection of awareness in aware subjects using EEG signals. Clin EEG Neurosci 2016; 47 (04) 266-275
48 Annen J, Laureys S, Gosseries O. Brain-computer interfaces for consciousness assessment and communication in severely brain-injured patients. Handb Clin Neurol 2020; 168: 137-152
49 Chatelle C, Spencer CA, Cash SS, Hochberg LR, Edlow BL. Feasibility of an EEG-based brain-computer interface in the intensive care unit. Clin Neurophysiol 2018; 129 (08) 1519-1525
50 Giacino JT, Katz DI, Schiff ND. et al. Practice guideline update recommendations summary: disorders of consciousness: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Neurology 2018; 91 (10) 450-460