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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Designing communication technologies based on physiological sensing
TL;DR: A system that aims to aid information workers in understanding how their attention varies during different tasks, and aids in scheduling and increased awareness is built, and a set of research probes are developed to explore how signals from the human body can be collected, visualized, and communicated.
Proceedings ArticleDOI
Decoding of motor imagery potentials in driving using DE-induced fuzzy-neural classifier
TL;DR: The fuzzy-recurrent neural network classifier offers better performance using proposed differential evolution-induced feature selection technique, when compared with principal component analysis in such situation and provides the highest classification accuracy of 98.472%.
Book ChapterDOI
Getting Around: Assistive Technology for Mobility
TL;DR: The choice of what type of mobility a child needs is down to their self-defined goals in the context of their school environment, family and general ecosystem.
Using a cVEP-based Brain-Computer Interface to control a virtual agent -- dataset
TL;DR: In this paper, the authors presented the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection, which can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds.
Dissertation
Evaluating a Novel Brain-Computer Interface and EEG Biomarkers For Cognitive Assessment in Children With Cerebral Palsy
TL;DR: This work developed an asynchronous, event-related potential BCI-facilitated administration procedure for the Peabody Picture Vocabulary Test (PPVT-IV), and tested the system in typically developing (TD) individuals, as well as people with cerebral palsy (CP) to compare results to the standardized PPVT- IV format and administration.
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.