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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.


Illustration of event-related desynchronization (ERD) of electroencephalographic (EEG) sensorimotor rhythm activity (SMR, 8-15Hz) related to motor imagery of hand closing motions in a representative participant during calibration. The red line indicates ERD during the instruction to rest (red square presentations), while the black line indicates ERD during the instruction to imagine hand-closing motions (green square presentations). ERD was calculated relative to a reference period at -1.5 to -0.5 s before the visual cue. The 95% confidence levels are shown as red and green areas, respectively. The discrimination threshold for detection of motor imagery-related ERD for BNCI control is indicated as red dotted line.
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Fig4: Illustration of event-related desynchronization (ERD) of electroencephalographic (EEG) sensorimotor rhythm activity (SMR, 8-15Hz) related to motor imagery of hand closing motions in a representative participant during calibration. The red line indicates ERD during the instruction to rest (red square presentations), while the black line indicates ERD during the instruction to imagine hand-closing motions (green square presentations). ERD was calculated relative to a reference period at -1.5 to -0.5 s before the visual cue. The 95% confidence levels are shown as red and green areas, respectively. The discrimination threshold for detection of motor imagery-related ERD for BNCI control is indicated as red dotted line.

Mentions: To identify the optimal frequency for detection of motor-imagery related desynchronization of sensorimotor rhythms (SMR, 8-15Hz) of each participant, a power spectrum estimation (autoregressive model of order 16 using the Yule–Walker algorithm) was performed for each incoming sample, selecting the frequency that showed largest even-related desynchronization (ERD) during motor imagery and event-related synchronization (ERS) during rest [13, 14] recorded from C3. Based on the maximum values for ERD and ERS, a discrimination threshold was set at two-standard deviations above average SMR-ERD variance at rest, and used for later online BNCI control (Figure 4).Figure 4


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)

Illustration of event-related desynchronization (ERD) of electroencephalographic (EEG) sensorimotor rhythm activity (SMR, 8-15Hz) related to motor imagery of hand closing motions in a representative participant during calibration. The red line indicates ERD during the instruction to rest (red square presentations), while the black line indicates ERD during the instruction to imagine hand-closing motions (green square presentations). ERD was calculated relative to a reference period at -1.5 to -0.5 s before the visual cue. The 95% confidence levels are shown as red and green areas, respectively. The discrimination threshold for detection of motor imagery-related ERD for BNCI control is indicated as red dotted line.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Illustration of event-related desynchronization (ERD) of electroencephalographic (EEG) sensorimotor rhythm activity (SMR, 8-15Hz) related to motor imagery of hand closing motions in a representative participant during calibration. The red line indicates ERD during the instruction to rest (red square presentations), while the black line indicates ERD during the instruction to imagine hand-closing motions (green square presentations). ERD was calculated relative to a reference period at -1.5 to -0.5 s before the visual cue. The 95% confidence levels are shown as red and green areas, respectively. The discrimination threshold for detection of motor imagery-related ERD for BNCI control is indicated as red dotted line.
Mentions: To identify the optimal frequency for detection of motor-imagery related desynchronization of sensorimotor rhythms (SMR, 8-15Hz) of each participant, a power spectrum estimation (autoregressive model of order 16 using the Yule–Walker algorithm) was performed for each incoming sample, selecting the frequency that showed largest even-related desynchronization (ERD) during motor imagery and event-related synchronization (ERS) during rest [13, 14] recorded from C3. Based on the maximum values for ERD and ERS, a discrimination threshold was set at two-standard deviations above average SMR-ERD variance at rest, and used for later online BNCI control (Figure 4).Figure 4

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.