Archives of Physical Medicine and Rehabilitation
Original articleSensorimotor Modulation Assessment and Brain-Computer Interface Training in Disorders of Consciousness
Section snippets
Participants
The study included 4 subjects based in Ireland: (1) E, a 27-year-old man who was treated for a juvenile posterior fossa astrocytoma 12 years ago and had postoperative complications that caused severe brain damage and a minimally conscious state (Coma Recovery Scale–Revised score, 4); (2) J, a 53-year-old man who had an anoxic brain injury 4 years ago that caused a minimally conscious state (Coma Recovery Scale–Revised score, 3); (3) P, a 30-year-old man who had severe head trauma 4 years ago
Initial assessment
The time course of mean classification accuracy for each subject in the initial assessment and a feedback repetition with high mean classification accuracy showed an increase from approximately 50% at baseline (<3s) toward a peak in the event-related period (table 1, fig 3). All subjects had significant differences between baseline mean classification accuracy (2s) and peak mean classification accuracy (P≤.05) in all cases in which the differences between peak and baseline (peak−baseline) range
Discussion
The evidence obtained from the initial assessment suggests that these subjects in a minimally conscious state were aware of themselves and the environment. BCIs use self-directed neurophysiological processes such as the activation of the sensorimotor cortex during motor imagery or attempted motor execution. The results observed in the initial assessment involving a cue, with instructions presented visually and verbally, suggest that subjects had the capacity for sustained attention, response
Conclusions
In patients who are in a minimally conscious state, the true level of awareness may not be known because the patient may be unable to provide overt motor responses. The present EEG-based assessment showed that subjects attempted to activate sensorimotor areas, and this suggests that these subjects had awareness and cognitive ability. Therefore, the accuracy of the diagnosis that these patients receive may be improved with an EEG-based assessment, and individuals who have disorders of
Suppliers
- a.
g.tec Medical Engineering GmbH.
- b.
National Instruments Corp.
- c.
Etymotic Research, Inc.
References (31)
- et al.
Bedside detection of awareness in the vegetative state: a cohort study
Lancet
(2011) - et al.
The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility
Arch Phys Med Rehabil
(2004) - et al.
Should the parameters of a BCI translation algorithm be continually adapted?
J Neurosci Methods
(2011) - et al.
Should the parameters of a BCI translation algorithm be continually adapted?
J Neurosci Methods
(2011) - et al.
Brain oscillations control hand orthosis in a tetraplegic
Neurosci Lett
(2000) - et al.
Clinical applications of brain-computer interfaces: current state and future prospects
IEEE Rev Biomed Eng
(2009) - et al.
Detecting awareness in the vegetative state
Science
(2006) - et al.
Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters
IEEE Trans Rehabil Eng
(1998) EEG event-related desynchronization (ERD) and event-related synchronization (ERS)
- et al.
A time-frequency approach to feature extraction for a brain-computer interface with a comparative analysis of performance measures
EURASIP J Adv Signal Process
(2005)
Games, gameplay, and BCI: the state of the art
IEEE Trans Comput Intell AI Games
Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study
J Neuroeng Rehabil
Enabling control in the minimally conscious state in a single session with a three channel BCI
Visual and stereo audio sensorimotor rhythm feedback in the minimally conscious state
Cited by (52)
Technological Modalities in the Assessment and Treatment of Disorders of Consciousness
2024, Physical Medicine and Rehabilitation Clinics of North AmericaVisual and haptic feedback in detecting motor imagery within a wearable brain–computer interface
2023, Measurement: Journal of the International Measurement ConfederationA comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions
2022, Artificial Intelligence-Based Brain-Computer InterfaceNeurofeedback with low-cost, wearable electroencephalography (EEG) reduces symptoms in chronic Post-Traumatic Stress Disorder
2021, Journal of Affective Disorders
Presented to the National Institutes of Health, National Science Foundation, and other organizations (for a full list, see http://bcimeeting.org/2013/sponsors.html), June 3-7, 2013, Asilomar Conference Grounds, Pacific Grove, CA.
Supported in part by the UK Engineering and Physical Sciences Research Council (grant no. EP/H012958/1) and a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellowship.
Coyle reports grants from Invest Northern Ireland (grant no. POC309), outside the submitted work. He has a patent New International PCT application No. PCT/GB2012/051312 claiming priority from UK patent application No. 1109638.5. The other authors have nothing to disclose.