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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 for grasping motions. The illustrated device was developed by The BioRobotics Institute (Scuola Superiore Sant’Anna, Pisa, Italy) to perform opening and closing motions of a hand [2]. A) full opening position. B) full closing position.
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Fig1: Hand exoskeleton for grasping motions. The illustrated device was developed by The BioRobotics Institute (Scuola Superiore Sant’Anna, Pisa, Italy) to perform opening and closing motions of a hand [2]. A) full opening position. B) full closing position.

Mentions: Real-time translation of brain activity into control signals of external devices, known today as brain-computer or brain-machine interfaces (BCI/BMI), can substantially enhance human-machine interaction (HMI) [1], e.g. allowing impaired individuals to operate assistive systems as hand prostheses or exoskeletons (Figure 1, [2, 3]). The main challenge of non-invasive BMIs relates to their accuracy in continuous detection of specific brain signals, which directly affects the system’s reliability and safety [1, 4]. Despite considerable efforts, e.g. implementation of intelligent machine learning algorithms [5, 6] or remarkable technical advances improving active BMI control [1, 3], classification accuracy of most BMI systems is still insufficient for many assistive applications, particularly those related to motor control, where misclassification can lead to unwanted actions and serious safety risks. A possible strategy to increase safety of brain-controlled assistive systems in daily life environments is to use a switch mechanism turning the BMI system off or into sleep mode when active brain control is not needed or desired [7, 8]. Moreover, recent studies combined different biosignals, e.g. EEG and EOG signals, to increase the degrees of freedom in control of external devices, e.g. to navigate a toy truck [9] or wheelchair [10]. It is unclear, though, whether fusion of bio-signals can also improve reliability and safety during ongoing, active brain control of a hand exoskeleton.Figure 1


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 for grasping motions. The illustrated device was developed by The BioRobotics Institute (Scuola Superiore Sant’Anna, Pisa, Italy) to perform opening and closing motions of a hand [2]. A) full opening position. B) full closing position.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: Hand exoskeleton for grasping motions. The illustrated device was developed by The BioRobotics Institute (Scuola Superiore Sant’Anna, Pisa, Italy) to perform opening and closing motions of a hand [2]. A) full opening position. B) full closing position.
Mentions: Real-time translation of brain activity into control signals of external devices, known today as brain-computer or brain-machine interfaces (BCI/BMI), can substantially enhance human-machine interaction (HMI) [1], e.g. allowing impaired individuals to operate assistive systems as hand prostheses or exoskeletons (Figure 1, [2, 3]). The main challenge of non-invasive BMIs relates to their accuracy in continuous detection of specific brain signals, which directly affects the system’s reliability and safety [1, 4]. Despite considerable efforts, e.g. implementation of intelligent machine learning algorithms [5, 6] or remarkable technical advances improving active BMI control [1, 3], classification accuracy of most BMI systems is still insufficient for many assistive applications, particularly those related to motor control, where misclassification can lead to unwanted actions and serious safety risks. A possible strategy to increase safety of brain-controlled assistive systems in daily life environments is to use a switch mechanism turning the BMI system off or into sleep mode when active brain control is not needed or desired [7, 8]. Moreover, recent studies combined different biosignals, e.g. EEG and EOG signals, to increase the degrees of freedom in control of external devices, e.g. to navigate a toy truck [9] or wheelchair [10]. It is unclear, though, whether fusion of bio-signals can also improve reliability and safety during ongoing, active brain control of a hand exoskeleton.Figure 1

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.