The hybrid Brain-Computer Interface: a bridge to assistive technology?

Müller-Putz, Gernot; Schreuder, Martijn; Tangermann, Michael; Leeb, Robert; del, R. J. Millán

doi:10.1515/bmt-2013-4435

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Furthermore, brain activity, e.g., recorded by electroencephalography (EEG), is highly nonstationary [13] requiring frequent recalibration that interferes with fluent, self-paced (asynchronous) control. Systems that combine or fuse different input signals to control computers or external devices were recently conceptualized as hybrid BMI [20] or hybrid brain/neural-computer interaction (BNCI) systems [8] and are currently broadly investigated [28].…”

Section: Introductionmentioning

confidence: 99%

An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand

Soekadar

Witkowski²,

Vitiello

et al. 2015

Biomedical Engineering / Biomedizinische Technik

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The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5 ± 3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24 ± 16.71%, BPI: 65.93 ± 24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65 ± 11.28, BPI: 76.03 ± 18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.

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Section: Introductionmentioning

confidence: 99%

An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand

Soekadar

Witkowski²,

Vitiello

et al. 2015

Biomedical Engineering / Biomedizinische Technik

View full text Add to dashboard Cite

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Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Lebedev

Nicolelis

2017

Physiological Reviews

477

288

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Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are ) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

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The hybrid Brain-Computer Interface: a bridge to assistive technology?

Cited by 2 publications

References 6 publications

An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand

An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

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