Prediction of consciousness recovery in patients with disorder of consciousness using Brain-computer Interface

The aim of this study is to determine the potential prognostic value of using Brain-computer Interface (BCI) to identify patients with disorder of consciousness (DOC), who show potential for recovery. A retrospective study involved 51 patients with DOC were conducted. Each patient conducted in a BCI experiment to detect awareness and received a 3-months follow-up. The BCI accuracies were correlated to patient outcomes according to the Coma Recovery Scale Revised (CRS-R). The statistical tests showed that patients with significant accuracies higher than the chance level showed greater improvement in CRS-R scores after 3 months compared to patients without significant accuracies. The sensitivity and specificity of the BCI accuracies in determining individual's consciousness recovery after 3 months are 67.7% and 90% respectively. A highly significant relationship between BCI accuracies and subsequent recovery was thus found. The BCI is suggested as an important tool to assess information-processing capacities that can predict the likelihood of recovery in patients with disorder of consciousness.

[1]  J. Giacino,et al.  The vegetative and minimally conscious states: current knowledge and remaining questions. , 2005, The Journal of head trauma rehabilitation.

[2]  Athena Demertzi,et al.  Is there anybody in there? Detecting awareness in disorders of consciousness , 2008, Expert review of neurotherapeutics.

[3]  Giuseppe Galardi,et al.  Usefulness of Standard EEG in Predicting the Outcome of Patients With Disorders of Consciousness After Anoxic Coma , 2011, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[4]  S. Scott,et al.  Detecting Residual Cognitive Function in Persistent Vegetative State , 2002, Neurocase.

[5]  H. Yin,et al.  Brain response to thermal stimulation predicts outcome of patients with chronic disorders of consciousness , 2015, Clinical Neurophysiology.

[6]  Yuanqing Li,et al.  A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control , 2013, IEEE Transactions on Biomedical Engineering.

[7]  Steven Laureys,et al.  Cerebral response to patient's own name in the vegetative and minimally conscious states , 2007, Neurology.

[8]  Zahra Rojhani Shirazi,et al.  Prediction of Consciousness Recovery in Coma after Traumatic Brain Injury by Disorder of Consciousness Scale (DOCS). , 2013, Bulletin of emergency and trauma.

[9]  Steven Laureys,et al.  Detecting awareness in patients with disorders of consciousness using a hybrid brain–computer interface , 2014, Journal of neural engineering.

[10]  C. Machado.  Cerebral processing in the minimally conscious state , 2005, Neurology.

[11]  F. Plum,et al.  Persistent vegetative state after brain damage. A syndrome in search of a name. , 1972, Lancet.

[12]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[13]  Yuanqing Li,et al.  A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection , 2015, Scientific Reports.

[14]  N. Birbaumer,et al.  Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? , 2008, Clinical Neurophysiology.

[15]  G. Galardi,et al.  Prognostic value of standard EEG in traumatic and non-traumatic disorders of consciousness following coma , 2010, Clinical Neurophysiology.

[16]  T. Pape,et al.  Predictive Value of the Disorders of Consciousness Scale (DOCS) , 2009, PM & R : the journal of injury, function, and rehabilitation.

[17]  Steven Laureys,et al.  Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system , 2015, BMC Neurology.

[18]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[19]  A. Sant'angelo,et al.  EEG predictors of outcome in patients with disorders of consciousness admitted for intensive rehabilitation , 2015, Clinical Neurophysiology.

[20]  M. Boly,et al.  Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment , 2009, BMC neurology.