ArticleComputational intelligence and neuroscience2016
Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.
Article in Computational intelligence and neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials.Sensors (Basel, Switzerland) · 2023Article
- Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.Medical & biological engineering & computing · 2023Article
- Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.Sensors (Basel, Switzerland) · 2022Article
- Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes.Research (Washington, D.C.) · 2022Article
- Use of Force Feedback Device in a Hybrid Brain-Computer Interface Based on SSVEP, EOG and Eye Tracking for Sorting Items.Sensors (Basel, Switzerland) · 2021Article
- Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.APL bioengineering · 2021Review
- Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain-Computer Interfaces.Brain sciences · 2020Article
- Brain-Computer Interface-Based Humanoid Control: A Review.Sensors (Basel, Switzerland) · 2020Review
- Object Extraction in Cluttered Environments via a P300-Based IFCE.Computational intelligence and neuroscience · 2017Article
- A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot.Journal of healthcare engineering · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be used for remote telepresence or remote driving. The communication channel to the target device is based on the steady-state visual evoked potentials. In order to test the control application, a mobile robotic car (MRC) was introduced and a four-class BCI graphical user interface (with live video feedback and stimulation boxes on the same screen) for piloting the MRC was designed. For the purpose of evaluating a potential real-life scenario for such assistive technology, we present a study where 61 subjects steered the MRC through a predetermined route. All 61 subjects were able to control the MRC and finish the experiment (mean time 207.08 s, SD 50.25) with a mean (SD) accuracy and ITR of 93.03% (5.73) and 14.07 bits/min (4.44), respectively. The results show that our proposed SSVEP-based BCI control application is suitable for mobile robots with a shared-control approach. We also did not observe any negative influence of the simultaneous live video feedback and SSVEP stimulation on the performance of the BCI system.
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Registered trials
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