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

Witkowski M, Cortese M, Cempini M, Mellinger J, Vitiello N, Soekadar SR - J Neuroeng Rehabil (2014)

Bottom Line: 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).EEG/EOG biosignal fusion can substantially enhance safety of assistive BNCI systems improving their applicability in daily life environments.

View Article: PubMed Central - PubMed

Affiliation: Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital Tübingen, 72076, Tübingen, Germany. surjo@soekadar.com.

ABSTRACT

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.

No MeSH data available.


Biosignals recorded by electroencephalography (EEG) and electrooculography (EOG) were used to control a hand-exoskeleton allowing for grasping motions. While under condition #1 only EEG signals were used for hand exoskeleton control, both EEG and EOG signals were used during condition #2.
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Fig2: Biosignals recorded by electroencephalography (EEG) and electrooculography (EOG) were used to control a hand-exoskeleton allowing for grasping motions. While under condition #1 only EEG signals were used for hand exoskeleton control, both EEG and EOG signals were used during condition #2.

Mentions: All participants were comfortably seated at a desk while EEG was recorded from 5 conventional EEG recording sites (F3, T3, C3, P3, and CZ according to the international 10/20 system) using an active electrode EEG system (Acti-cap® and BrainAmp®, BrainProducts, Gilching, Germany) with a reference electrode placed at FCz and ground electrode at AFz. EEG was recorded at a sampling rate of 200Hz, bandpass filtered at 0.4-70Hz and pre-processed using a small Laplacian filter. EOG was recorded in accordance to the standard EOG placements at the left and right outer canthus (LOC/ROC) (Figure 2).


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

Witkowski M, Cortese M, Cempini M, Mellinger J, Vitiello N, Soekadar SR - J Neuroeng Rehabil (2014)

Biosignals recorded by electroencephalography (EEG) and electrooculography (EOG) were used to control a hand-exoskeleton allowing for grasping motions. While under condition #1 only EEG signals were used for hand exoskeleton control, both EEG and EOG signals were used during condition #2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4274709&req=5

Fig2: Biosignals recorded by electroencephalography (EEG) and electrooculography (EOG) were used to control a hand-exoskeleton allowing for grasping motions. While under condition #1 only EEG signals were used for hand exoskeleton control, both EEG and EOG signals were used during condition #2.
Mentions: All participants were comfortably seated at a desk while EEG was recorded from 5 conventional EEG recording sites (F3, T3, C3, P3, and CZ according to the international 10/20 system) using an active electrode EEG system (Acti-cap® and BrainAmp®, BrainProducts, Gilching, Germany) with a reference electrode placed at FCz and ground electrode at AFz. EEG was recorded at a sampling rate of 200Hz, bandpass filtered at 0.4-70Hz and pre-processed using a small Laplacian filter. EOG was recorded in accordance to the standard EOG placements at the left and right outer canthus (LOC/ROC) (Figure 2).

Bottom Line: 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).EEG/EOG biosignal fusion can substantially enhance safety of assistive BNCI systems improving their applicability in daily life environments.

View Article: PubMed Central - PubMed

Affiliation: Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital Tübingen, 72076, Tübingen, Germany. surjo@soekadar.com.

ABSTRACT

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.

No MeSH data available.