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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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Shared control of a brain-actuated intelligent wheelchair

TL;DR: A two-layer shared-control strategy is proposed to steer the intelligent wheelchair by means of a noninvasive Brain-computer Interface and a navigation system to validate user's commands to achieve efficient navigation and position under condition of decoding uncertainly of BCI.
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Brainwave-Controlled System for Smart Home Applications

TL;DR: The developed electroencephalogram (EEG)-controlled system for a smart home that is intended to assist disabled and elderly people and has high potential to be used in smart automation and wireless biomedical applications.
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Phase-amplitude coupling between mu- and gamma-waves to carry motor commands

TL;DR: It is suggested that mu-activity acts as a carrier of gamma-activity between inferior and parietal areas utilising phase-amplitude coupling.
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EEG Controlled Wheelchair

TL;DR: The main objective of this project is to construct a wheelchair which can be directly controlled by the brain without requires any physical feedback as controlling input from the user.
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Hybrid brain/muscle-actuated control of an intelligent wheelchair

TL;DR: This paper presents a real-time hybrid brain/muscle interface to control a wheelchair directly to keep the disables recovering several motion capabilities by using noninvasive motor imagery Electroencephalography (EEG) and Electromyography (EMG).
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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