A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots.
Ferran Galán,Ferran Galán,Marnix Nuttin,Eileen Lew,Pierre W. Ferrez,G. Vanacker,Johan Philips,J. del R. Millan,J. del R. Millan +8 more
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TLDR
The results show that subjects can rapidly master the authors' asynchronous EEG-based BCI to control a wheelchair and can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities.About:
This article is published in Clinical Neurophysiology.The article was published on 2008-09-01 and is currently open access. It has received 644 citations till now. The article focuses on the topics: Wheelchair.read more
Figures
Fig. 6. Top view of the random paths in Task 2. Trial 1 placed in upper row, first column. Trial 10 placed in second row, last column. 
Fig. 7. Top view of the world and the path stretches. Stretches F1 and F2 were labelled as Forward, R1 and R2 labelled as Right, L labelled as Left, and SD1 and SD2 labelled as strategy dependent. The subjects can go through SD1 by means of two strategies, either executing Forward or executing Right followed by Left. Through SD2, subjects can execute either Forward or Left followed by Right. 
Table 1 LDA train-test classification accuracies on the configuration 
Fig. 1. (Left) Monitor display in a first person view from the Start. The white cursor at the center is the fixation point. The rectangle at the bottom is the simulated wheelchair. (Right) Top view of the simulated world and the pre-specified path. 
Fig. 8. Percentage of reached final goals over sessions. The time elapsed between sessions was: 1 day between sessions 1 and 2, 2 months between sessions 2 and 3, 1 h between sessions 3 and 4, and 1 day between sessions 4 and 5. 
Fig. 2. Electrode discrimination index values De (see Section 2.3.2) for the selected frequencies for each subject, and the associated scalp distribution of the averaged logarithmic transform of the power spectral density, Log(PSDe), for each class. For subject 1, De is higher at left temporal, central and right occipital areas. For subject 2, at 10 Hz it is higher at right centro-parietal areas, and at 12 and 14 Hz it is higher at bilateral parietal areas. These areas correspond with those where the differences between the averaged Log(PSDe) patterns associated to each mental task is the biggest.
Citations
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Journal ArticleDOI
White Matter Connectivity Pattern Associate with Characteristics of Scalp EEG Signals.
Jinnan Gong,Cheng Luo,Xuebin Chang,Rui Zhang,Benjamin Klugah-Brown,Lanjin Guo,Peng Xu,Dezhong Yao +7 more
TL;DR: The analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 have a significant impact on ERDs when doing MI, which provided the coupling mechanism between structural and dynamic physiological features of human brain.
Implementation of a wheelchair control using a four- command brain computer interface
Alexandre Ormiga,Galvão Barbosa,Daniel Zacarias Freitas,Marco Antonio Meggiolaro,Pontifical Catholic +4 more
TL;DR: This work presents the development of a brain computer interface as an alternative communication channel to be used in assistive equipment, and proposes two implementations of the developed classifiers to improve the rate of successful commands to the electric wheelchair.
Sub-Band-Power-Based Efficient Brain Computer Interface for Wheelchair Control
TL;DR: An efficient Brain Computer Interface is designed and implemented to allow disabled people to control the motion of wheelchairs that uses a compact portable EEG sensor to capture 14 brain signals and wirelessly feed them to the PC.
DissertationDOI
Optimizing Spatial Filters to reduce BCI Inefficiency
TL;DR: This work is the first attempt to build an overview on BCI inefficiency for electroencephalogram (EEG) BCIs based on sensorimotor rhythm (SMR) to better understand and face it and develops a new spatial filter algorithm called Common Spatial Pattern Patches (CSPP), a compromise between Laplacian filters and CSP.
Book ChapterDOI
Intelligent Human–Robot Assembly Enabled by Brain EEG
Abdullah Mohammed,Lihui Wang +1 more
TL;DR: In this article, a framework that can facilitate the interactions between a human's EEG (electroencephalography) signals and an industrial robot is presented. But it is not applicable to other types of robots, such as those used for assisting people with severe disability.
References
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Journal ArticleDOI
Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates
Jose M. Carmena,Mikhail A. Lebedev,Roy E. Crist,Joseph E. O'Doherty,David M. Santucci,Dragan F. Dimitrov,Parag G. Patil,Craig S. Henriquez,Miguel A. L. Nicolelis +8 more
TL;DR: It is demonstrated that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters from the electrical activity of frontoparietal neuronal ensembles.
Journal ArticleDOI
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
TL;DR: It is shown that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys.
Journal ArticleDOI
A spelling device for the paralysed
Niels Birbaumer,N. Ghanayim,Thilo Hinterberger,Iver H. Iversen,Boris Kotchoubey,Andrea Kübler,J. Perelmouter,Edward Taub,Herta Flor +8 more
TL;DR: A new means of communication for the completely paralysed that uses slow cortical potentials of the electro-encephalogram to drive an electronic spelling device is developed.