Journal ArticleDOI
EEG-Based Brain-Controlled Mobile Robots: A Survey
Luzheng Bi,Xin-an Fan,Yili Liu +2 more
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TLDR
A comprehensive review of the complete systems, key techniques, and evaluation issues of brain-controlled mobile robots along with some insights into related future research and development issues is provided.Abstract:
EEG-based brain-controlled mobile robots can serve as powerful aids for severely disabled people in their daily life, especially to help them move voluntarily. In this paper, we provide a comprehensive review of the complete systems, key techniques, and evaluation issues of brain-controlled mobile robots along with some insights into related future research and development issues. We first review and classify various complete systems of brain-controlled mobile robots into two categories from the perspective of their operational modes. We then describe key techniques that are used in these brain-controlled mobile robots including the brain-computer interface techniques and shared control techniques. This description is followed by an analysis of the evaluation issues of brain-controlled mobile robots including participants, tasks and environments, and evaluation metrics. We conclude this paper with a discussion of the current challenges and future research directions.read more
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Journal ArticleDOI
Asynchronous Brain–Computer Interfacing Based on Mixed-Coded Visual Stimuli
Kaori Suefusa,Toshihisa Tanaka +1 more
TL;DR: The present study showed that an asynchronous BCI can be implemented with mixed-coded visual stimuli for the first time, which enables a large increase in the number of choices/commands.
Journal ArticleDOI
Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.
TL;DR: A thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection and classification techniques used, and wheelchair components and performance evaluation, provides state-of-the-art applications of EEG-based BCIs to wheelchair control and movement.
Proceedings ArticleDOI
Assistive robot operated via P300-based brain computer interface
TL;DR: An architecture for the operation of an assistive robot finally aimed at allowing users with severe motion disabilities to perform manipulation tasks that may help in daily-life operations is presented.
Journal ArticleDOI
Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves
TL;DR: Experimental tests proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%.
Journal ArticleDOI
Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals
TL;DR: This paper provides a comprehensive review of EEG signal processing in robot control, including mobile robots and robotic arms, especially based on noninvasive brain computer interface systems.
References
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EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Arnaud Delorme,Scott Makeig +1 more
TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
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Journal ArticleDOI
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
TL;DR: The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec, which means that subjects can communicate 12.0 bits, or 2.3 characters, per min.
Journal ArticleDOI
Removing electroencephalographic artifacts by blind source separation.
Tzyy-Ping Jung,Tzyy-Ping Jung,Scott Makeig,Colin Humphries,Te-Won Lee,Te-Won Lee,Martin J. McKeown,Vicente J. Iragui,Terrence J. Sejnowski,Terrence J. Sejnowski +9 more
TL;DR: The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
Journal ArticleDOI
BCI2000: a general-purpose brain-computer interface (BCI) system
TL;DR: This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system.