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
Citations
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Brain-Computer Interfaces and Augmented Reality: A State of the Art
TL;DR: This paper reviews the state of the art of using Brain-Computer Interfaces (BCIs) in combination with Augmented Reality (AR) and describes the various systems designed so far, categorized by their application field: medicine, robotics, home automation and brain activity visualization.
Journal ArticleDOI
EEG Artifacts Handling in a Real Practical Brain–Computer Interface Controlled Vehicle
TL;DR: The results indicate that the artifacts can highly affect the system performance; reducing their influence significantly improves the efficiency.
Journal ArticleDOI
Introducing the Edges Paradigm: A P300 Brain–Computer Interface for Spelling Written Words
TL;DR: A new P300 speller is introduced-the edges paradigm (EP), which exhibited attenuated influences of crowding and adjacency-problems known to perturb the RCP, alongside a faster communication rate.
Journal ArticleDOI
Novel Classification System for Classifying Cognitive Workload Levels Under Vague Visual Stimulation
TL;DR: This paper presents a novel method for classifying four different levels of cognitive workload generated using visual stimuli degradation and shows that the proposed solution is more accurate and computationally less demanding.
Proceedings ArticleDOI
An experiment of lie detection based EEG-P300 classified by SVM algorithm
TL;DR: Despite being relatively low in accuracy, the resulting model that is used in the program proved to be able to discern all of the subjects.
References
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Journal ArticleDOI
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.
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.
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.