Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG)

Background: brain-machine interfaces (bmis) allow direct translation of electric,magnetic or metabolic brain signals into control commands of external devices such as robots,prostheses or exoskeletons. however,non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of bmis,particularly in daily life environments. here we introduce and tested a novel hybrid brain-neural computer interaction (bnci) system fusing electroencephalography (eeg) and electrooculography (eog) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions. findings: 12 healthy volunteers (8 male,mean age 28.1 ± 3.63y) used eeg (condition #1) and hybrid eeg/eog (condition #2) signals to control a hand exoskeleton. motor imagery-related brain activity was translated into exoskeleton-driven hand closing motions. unintended motions could be interrupted by eye movement-related eog signals. in order to evaluate bnci control and safety,participants were instructed to follow a visual cue indicating either to move or not to move the hand exoskeleton in a random order. movements exceeding 25% of a full grasping motion when the device was not supposed to be moved were defined as safety violation. while participants reached comparable control under both conditions,safety was frequently violated under condition #1 (eeg),but not under condition #2 (eeg/eog). conclusion: eeg/eog biosignal fusion can substantially enhance safety of assistive bnci systems improving their applicability in daily life environments. © 2014 witkowski et al.; licensee biomed central ltd.