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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.


Hand exoskeleton-closing motions in % relative to a full closing motion during EEG control (condition #1, left side) and hybrid EEG/EOG BNCI control (condition #2, right side) averaged across all participants while green or red squares were presented. Participants were instructed to close the hand exoskeleton during green square presentations (black circles/crosses), and not to move during red square presentations (red circles/crosses). All participants were able to successfully close the device and reached successful control during green square presentations. However, during condition #1, the safety threshold (set at 25% closing motions during red square presentations) was often exceeded, but only once under condition #2.
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Fig5: Hand exoskeleton-closing motions in % relative to a full closing motion during EEG control (condition #1, left side) and hybrid EEG/EOG BNCI control (condition #2, right side) averaged across all participants while green or red squares were presented. Participants were instructed to close the hand exoskeleton during green square presentations (black circles/crosses), and not to move during red square presentations (red circles/crosses). All participants were able to successfully close the device and reached successful control during green square presentations. However, during condition #1, the safety threshold (set at 25% closing motions during red square presentations) was often exceeded, but only once under condition #2.

Mentions: All participants showed significant SMR-ERD during motor imagery and reached successful control of the BNCI system under both conditions. During presentation of the green square, the exoskeleton was closed in average by 63.59 ± 10.81% under condition #1 (EEG only) and 60.77 ± 9.42% under condition #2 (hybrid EEG/EOG control). While the exoskeleton closed the participants’ hand during red square presentations by 36.11 ± 10.85% under condition #1 (EEG only), the participants’ hand was closed by 12.31 ± 5.39% in average under condition #2 (hybrid EEG/EOG control) (Figure 5). During condition #2, participants used EOG signals in average in 60.9 ± 19.76% of trials.Figure 5


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)

Hand exoskeleton-closing motions in % relative to a full closing motion during EEG control (condition #1, left side) and hybrid EEG/EOG BNCI control (condition #2, right side) averaged across all participants while green or red squares were presented. Participants were instructed to close the hand exoskeleton during green square presentations (black circles/crosses), and not to move during red square presentations (red circles/crosses). All participants were able to successfully close the device and reached successful control during green square presentations. However, during condition #1, the safety threshold (set at 25% closing motions during red square presentations) was often exceeded, but only once under condition #2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Hand exoskeleton-closing motions in % relative to a full closing motion during EEG control (condition #1, left side) and hybrid EEG/EOG BNCI control (condition #2, right side) averaged across all participants while green or red squares were presented. Participants were instructed to close the hand exoskeleton during green square presentations (black circles/crosses), and not to move during red square presentations (red circles/crosses). All participants were able to successfully close the device and reached successful control during green square presentations. However, during condition #1, the safety threshold (set at 25% closing motions during red square presentations) was often exceeded, but only once under condition #2.
Mentions: All participants showed significant SMR-ERD during motor imagery and reached successful control of the BNCI system under both conditions. During presentation of the green square, the exoskeleton was closed in average by 63.59 ± 10.81% under condition #1 (EEG only) and 60.77 ± 9.42% under condition #2 (hybrid EEG/EOG control). While the exoskeleton closed the participants’ hand during red square presentations by 36.11 ± 10.85% under condition #1 (EEG only), the participants’ hand was closed by 12.31 ± 5.39% in average under condition #2 (hybrid EEG/EOG control) (Figure 5). During condition #2, participants used EOG signals in average in 60.9 ± 19.76% of trials.Figure 5

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.