Study design: Survey and long-term clinical post-trial follow-up (interviews/correspondence) on nine chronic, post spinal cord injury (SCI) tetraplegics.
Objective: To assess feasibility of the use of Electroencephalography-based Brain-Computer Interface (EEG-BCI) for reaching/grasping assistance in tetraplegics, through a robotic arm.
Settings: Physical and (neuromuscular) Rehabilitation Medicine, Cardiology, Neurosurgery Clinic Divisions of TEHBA and UMPCD, in collaboration with 'Brain2Robot' (composed of the European Commission-funded Marie Curie Excellence Team by the same name, hosted by Fraunhofer Institute-FIRST), in the second part of 2008.
Methods: Enrolled patients underwent EEG-BCI preliminary training and robot control sessions. Statistics entailed multiple linear regressions and cluster analysis. A follow-up-custom questionnaire based-including patients' perception of their EEG-BCI control capacity was continued up to 14 months after initial experiments.
Results: EEG-BCI performance/calibration-phase classification accuracy averaged 81.0%; feedback training sessions averaged 70.5% accuracy for 7 subjects who completed at least one feedback training session; 7 (77.7%) of 9 subjects reported having felt control of the cursor; and 3 (33.3%) subjects felt that they were also controlling the robot through their movement imagination. No significant side effects occurred. BCI performance was positively correlated with beta (13-30 Hz) EEG spectral power density (coefficient 0.432, standardized coefficient 0.745, P-value=0.025); another possible influence was sensory AIS score (range: 0 min to 224 max, coefficient -0.177, standardized coefficient -0.512, P=0.089).
Conclusion: Limited but real potential for self-assistance in chronic tetraplegics by EEG-BCI-actuated mechatronic devices was found, which was mainly related to spectral density in the beta range positively (increasing therewith) and to AIS sensory score negatively.