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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.


Experimental design: after calibration, all participants controlled the BNCI system under two conditions. During condition #1, EEG was used, while during condition #2 merged EEG and EOG signals were used for BNCI control of the hand exoskeleton. During EEG calibration, either a red square (indicating to rest) or green square (indicating to engage in motor-imagery) was shown. For EOG calibration, participants were asked to either look to the left (blue arrow to the left) or to the right (blue arrow to the right). For evaluation of BNCI control, a visual cue indicated not to move (red square) or to close the hand exoskeleton (green square) over 6 minutes in a random order. Visual indications were separated by inter-trial-intervals (ITIs) of 4-6 sec.
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Fig3: Experimental design: after calibration, all participants controlled the BNCI system under two conditions. During condition #1, EEG was used, while during condition #2 merged EEG and EOG signals were used for BNCI control of the hand exoskeleton. During EEG calibration, either a red square (indicating to rest) or green square (indicating to engage in motor-imagery) was shown. For EOG calibration, participants were asked to either look to the left (blue arrow to the left) or to the right (blue arrow to the right). For evaluation of BNCI control, a visual cue indicated not to move (red square) or to close the hand exoskeleton (green square) over 6 minutes in a random order. Visual indications were separated by inter-trial-intervals (ITIs) of 4-6 sec.

Mentions: To rule out overt movements during motor imagery, electromyography (EMG) was recorded from the right first dorsal interosseus muscle (FDI), extensor digitorum communis (EDC), extensor carpi ulnaris (ECU) and flexor carpi radialis (FCR). Skin/electrode resistance was kept below 12 kΩ. EMG signals were sampled at 1 kHz, and passed through a high-pass filter at 2 Hz (BrainAmp ExG®, Brainproducts, Gilching, Germany). If EMG activity exceeded a threshold of two standard deviations above the EMG signal recorded at rest, an auditory warning tone was given and data recorded during the warning tone was excluded. A custom version of BCI2000, a multipurpose standard BMI platform [12], was used for calibration and online BNCI control. Calibration of the BNCI system was performed once at the beginning of the session and kept unvaried for the rest of the session, and comprised two parts: in the first part, participants were instructed to either rest or imagine hand grasping motions following a visual cue (red square: REST, green square: GO) displayed on a computer screen (Figure 3).Figure 3


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)

Experimental design: after calibration, all participants controlled the BNCI system under two conditions. During condition #1, EEG was used, while during condition #2 merged EEG and EOG signals were used for BNCI control of the hand exoskeleton. During EEG calibration, either a red square (indicating to rest) or green square (indicating to engage in motor-imagery) was shown. For EOG calibration, participants were asked to either look to the left (blue arrow to the left) or to the right (blue arrow to the right). For evaluation of BNCI control, a visual cue indicated not to move (red square) or to close the hand exoskeleton (green square) over 6 minutes in a random order. Visual indications were separated by inter-trial-intervals (ITIs) of 4-6 sec.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Experimental design: after calibration, all participants controlled the BNCI system under two conditions. During condition #1, EEG was used, while during condition #2 merged EEG and EOG signals were used for BNCI control of the hand exoskeleton. During EEG calibration, either a red square (indicating to rest) or green square (indicating to engage in motor-imagery) was shown. For EOG calibration, participants were asked to either look to the left (blue arrow to the left) or to the right (blue arrow to the right). For evaluation of BNCI control, a visual cue indicated not to move (red square) or to close the hand exoskeleton (green square) over 6 minutes in a random order. Visual indications were separated by inter-trial-intervals (ITIs) of 4-6 sec.
Mentions: To rule out overt movements during motor imagery, electromyography (EMG) was recorded from the right first dorsal interosseus muscle (FDI), extensor digitorum communis (EDC), extensor carpi ulnaris (ECU) and flexor carpi radialis (FCR). Skin/electrode resistance was kept below 12 kΩ. EMG signals were sampled at 1 kHz, and passed through a high-pass filter at 2 Hz (BrainAmp ExG®, Brainproducts, Gilching, Germany). If EMG activity exceeded a threshold of two standard deviations above the EMG signal recorded at rest, an auditory warning tone was given and data recorded during the warning tone was excluded. A custom version of BCI2000, a multipurpose standard BMI platform [12], was used for calibration and online BNCI control. Calibration of the BNCI system was performed once at the beginning of the session and kept unvaried for the rest of the session, and comprised two parts: in the first part, participants were instructed to either rest or imagine hand grasping motions following a visual cue (red square: REST, green square: GO) displayed on a computer screen (Figure 3).Figure 3

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.