Damien Coyle1, Jacqueline Stow2, Karl McCreadie3, Jacinta McElligott2, Áine Carroll2. 1. Intelligent Systems Research Centre, University of Ulster, Derry, Northern Ireland, United Kingdom. Electronic address: dh.coyle@ulster.ac.uk. 2. National Rehabilitation Hospital, Dun Laoghaire, Republic of Ireland. 3. Intelligent Systems Research Centre, University of Ulster, Derry, Northern Ireland, United Kingdom.
Abstract
OBJECTIVES: To assess awareness in subjects who are in a minimally conscious state by using an electroencephalogram-based brain-computer interface (BCI), and to determine whether these patients may learn to modulate sensorimotor rhythms with visual feedback, stereo auditory feedback, or both. DESIGN: Initial assessment included imagined hand movement or toe wiggling to activate sensorimotor areas and modulate brain rhythms in 90 trials (4 subjects). Within-subject and within-group analyses were performed to evaluate significant activations. A within-subject analysis was performed involving multiple BCI technology training sessions to improve the capacity of the user to modulate sensorimotor rhythms through visual and auditory feedback. SETTING: Hospital, homes of subjects, and a primary care facility. PARTICIPANTS: Subjects (N=4; 3 men, 1 woman) who were in a minimally conscious state (age range, 27-53 y; 1-12 y after brain injury). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Awareness detection was determined from sensorimotor patterns that differed for each motor imagery task. BCI performance was determined from the mean classification accuracy of brain patterns by using a BCI signal processing framework and assessment of performance in multiple sessions. RESULTS: All subjects demonstrated significant and appropriate brain activation during the initial assessment, and real-time feedback was provided to improve arousal. Consistent activation was observed in multiple sessions. CONCLUSIONS: The electroencephalogram-based assessment showed that patients in a minimally conscious state may have the capacity to operate a simple BCI-based communication system, even without any detectable volitional control of movement.
OBJECTIVES: To assess awareness in subjects who are in a minimally conscious state by using an electroencephalogram-based brain-computer interface (BCI), and to determine whether these patients may learn to modulate sensorimotor rhythms with visual feedback, stereo auditory feedback, or both. DESIGN: Initial assessment included imagined hand movement or toe wiggling to activate sensorimotor areas and modulate brain rhythms in 90 trials (4 subjects). Within-subject and within-group analyses were performed to evaluate significant activations. A within-subject analysis was performed involving multiple BCI technology training sessions to improve the capacity of the user to modulate sensorimotor rhythms through visual and auditory feedback. SETTING: Hospital, homes of subjects, and a primary care facility. PARTICIPANTS: Subjects (N=4; 3 men, 1 woman) who were in a minimally conscious state (age range, 27-53 y; 1-12 y after brain injury). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Awareness detection was determined from sensorimotor patterns that differed for each motor imagery task. BCI performance was determined from the mean classification accuracy of brain patterns by using a BCI signal processing framework and assessment of performance in multiple sessions. RESULTS: All subjects demonstrated significant and appropriate brain activation during the initial assessment, and real-time feedback was provided to improve arousal. Consistent activation was observed in multiple sessions. CONCLUSIONS: The electroencephalogram-based assessment showed that patients in a minimally conscious state may have the capacity to operate a simple BCI-based communication system, even without any detectable volitional control of movement.
Authors: Jane E Huggins; Christoph Guger; Mounia Ziat; Thorsten O Zander; Denise Taylor; Michael Tangermann; Aureli Soria-Frisch; John Simeral; Reinhold Scherer; Rüdiger Rupp; Giulio Ruffini; Douglas K R Robinson; Nick F Ramsey; Anton Nijholt; Gernot Müller-Putz; Dennis J McFarland; Donatella Mattia; Brent J Lance; Pieter-Jan Kindermans; Iñaki Iturrate; Christian Herff; Disha Gupta; An H Do; Jennifer L Collinger; Ricardo Chavarriaga; Steven M Chase; Martin G Bleichner; Aaron Batista; Charles W Anderson; Erik J Aarnoutse Journal: Brain Comput Interfaces (Abingdon) Date: 2017-01-30