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Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic

Authors:

Doron Friedman,

Gernot R. Müller-Putz,

Reinhold Scherer,

Gert PfurtschellerAuthors Info & Claims

Computational Intelligence and Neuroscience, Volume 2007

Article No.: 7, Pages 1 - 12

https://doi.org/10.1155/2007/79642

Published: 01 April 2007 Publication History

Abstract

The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to "go" from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

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https://dl.acm.org/doi/10.1145/3603621
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Index Terms

Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic

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Published In

cover image Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience Volume 2007, Issue

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

April 2007

86 pages

ISSN:1687-5265

EISSN:1687-5273

Issue’s Table of Contents

Publisher

Hindawi Limited

London, United Kingdom

Publication History

Accepted: 17 July 2007

Published: 01 April 2007

Received: 18 February 2007

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Cited By

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https://dl.acm.org/doi/10.1145/3603621
Nandikolla VGhoslin BMatsuno KMedina Portilla D(2022)A Brain-Computer Interface for Teleoperation of a Semiautonomous Mobile Robotic Assistive System Using SLAMJournal of Robotics10.1155/2022/61789172022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6178917
Wang JShao HYao YLiu JMa S(2021)A Personalized Feature Extraction and Classification Method for Motor Imagery RecognitionMobile Networks and Applications10.1007/s11036-021-01754-026:3(1359-1371)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11036-021-01754-0
Wang FZhang XFu RGomez-Pilar J(2020)Research on Home-Auxiliary Robot System Based on Characteristics of Human Physiological and Motion SignalsComplexity10.1155/2020/81958932020Online publication date: 1-Jan-2020
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Huang XXue XYuan Z(2020)A Simulation Platform for the Brain-Computer Interface (BCI) Based Smart WheelchairArtificial Intelligence and Security10.1007/978-3-030-57884-8_23(257-266)Online publication date: 17-Jul-2020
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Ramírez-Moreno MGutiérrez DKim H(2019)Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial VisionComputational Intelligence and Neuroscience10.1155/2019/93748022019Online publication date: 1-Jan-2019
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Alchalabi BFaubert J(2019)A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual RealityComputational Intelligence and Neuroscience10.1155/2019/25034312019Online publication date: 1-Jan-2019
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