ArticleFrontiers in neuroscience2013
A BMI-based occupational therapy assist suit: asynchronous control by SSVEP.
Article in Frontiers in neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 62 citations in OpenAlex.
- A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface.GigaScience · 2019Trial
- A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.Sensors (Basel, Switzerland) · 2024Review
- Eyes-Free Tongue Gesture and Tongue Joystick Control of a Five DOF Upper-Limb Exoskeleton for Severely Disabled Individuals.Frontiers in neuroscience · 2021Article
- Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.Frontiers in human neuroscience · 2021Review
- Vision-aided brain-machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury.Biomedical engineering online · 2019Article
- Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects.Frontiers in human neuroscience · 2018Article
- Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking.Scientific reports · 2017Article
- Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.Frontiers in neuroscience · 2017Article
- Progress in EEG-Based Brain Robot Interaction Systems.Computational intelligence and neuroscience · 2017Review
- Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.Scientific reports · 2016Article
- Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.Frontiers in neuroscience · 2016Review
- An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary.Frontiers in neuroscience · 2016Article
- A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.Biomedical engineering online · 2015Article
- Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG).Journal of neuroengineering and rehabilitation · 2014Article
- When "I" becomes "We": ethical implications of emerging brain-to-brain interfacing technologies.Frontiers in neuroengineering · 2014Article
- A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography.BioMed research international · 2014Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A brain-machine interface (BMI) is an interface technology that uses neurophysiological signals from the brain to control external machines. Recent invasive BMI technologies have succeeded in the asynchronous control of robot arms for a useful series of actions, such as reaching and grasping. In this study, we developed non-invasive BMI technologies aiming to make such useful movements using the subject's own hands by preparing a BMI-based occupational therapy assist suit (BOTAS). We prepared a pre-recorded series of useful actions-a grasping-a-ball movement and a carrying-the-ball movement-and added asynchronous control using steady-state visual evoked potential (SSVEP) signals. A SSVEP signal was used to trigger the grasping-a-ball movement and another SSVEP signal was used to trigger the carrying-the-ball movement. A support vector machine was used to classify EEG signals recorded from the visual cortex (Oz) in real time. Untrained, able-bodied participants (n = 12) operated the system successfully. Classification accuracy and time required for SSVEP detection were ~88% and 3 s, respectively. We further recruited three patients with upper cervical spinal cord injuries (SCIs); they also succeeded in operating the system without training. These data suggest that our BOTAS system is potentially useful in terms of rehabilitation of patients with upper limb disabilities.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.