Brain-Computer Interfaces in Disorders of Consciousness

He, Qiheng; He, Jianghong; Yang, Yi; Zhao, Jizong

doi:10.1007/s12264-022-00920-y

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…We conducted a systematic review to evaluate the current state of BCI research with DoC patients. Although many reviews have been published about BCI and DoC, these are mainly narrative and non-systematic overviews about the advances in the field of the neurophysiology of DoC, including the BCI as one of the neurophysiological tools available to support the assessment of consciousness (Kübler and Kotchoubey, 2007 ; Kübler, 2009 ; Chatelle et al, 2012 ; Naci et al, 2012 ; Mikołajewska and Mikołajewski, 2014 ; Luauté et al, 2015 ; Gibson et al, 2016 ; Li et al, 2016 ; Annen et al, 2020a ; Comanducci et al, 2020 ; He et al, 2022 ; Xu et al, 2022 ). Therefore, the present work is specifically focused on studies testing a BCI on a sample of participants including at least one DoC patient.…”

Section: Introductionmentioning

confidence: 99%

EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review

Galiotta

Quattrociocchi

D’Ippolito

et al. 2022

Front. Hum. Neurosci.

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BackgroundDisorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess “covert” awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs).ObjectivesThe aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI.MethodsThe search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient.ResultsAmong the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.ConclusionDespite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.

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