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Journal ArticleDOI

EEG-Based Brain-Controlled Mobile Robots: A Survey

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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.

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Journal ArticleDOI

Queuing Network Modeling of Driver EEG Signals-Based Steering Control

TL;DR: This study provides some insights into the simulation and prediction of the performance of using BCI systems to control other external devices (e.g., mobile robots) and has potential values in helping develop a brain-controlled assistive vehicle.
Journal ArticleDOI

Model Predictive-Based Shared Control for Brain-Controlled Driving

TL;DR: A new shared control method based on the model predictive control (MPC) strategy is proposed, designed by introducing a penalty on the deviation from drivers output in the cost function and setting safety constraints to improve the performance of brain-controlled vehicles.
Proceedings ArticleDOI

Assistive robot operated via P300-based Brain Computer Interface.

TL;DR: In this article, the authors present 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.
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Social Cognitive and Affective Neuroscience in Human–Machine Systems: A Roadmap for Improving Training, Human–Robot Interaction, and Team Performance

TL;DR: This paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of human-machine systems with the pursuit of further research in this vein.
Journal ArticleDOI

Comparing interaction techniques for serious games through brain–computer interfaces: A user perception evaluation study

TL;DR: Recorded feedback indicates that the current state of BCIs can be used in the future as alternative game interfaces after familiarisation and in some cases calibration.
References
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Journal ArticleDOI

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

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.
Journal ArticleDOI

Event-related EEG/MEG synchronization and desynchronization: basic principles.

TL;DR: Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously.
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.

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.
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