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Open AccessJournal ArticleDOI

Three-Dimensional Brain–Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks

TLDR
The results strongly support the hypothesis that noninvasive EEG-based BCI can provide robust 3-D control through endogenous neural modulation in broader populations with limited training.
Abstract
Objective: While noninvasive electroenceph-alography (EEG) based brain-computer interfacing (BCI) has been successfully demonstrated in two-dimensional (2-D) control tasks, little work has been published regarding its extension to practical three-dimensional (3-D) control. Methods: In this study, we developed a new BCI approach for 3-D control by combining a novel form of endogenous visuospatial attentional modulation, defined as overt spatial attention (OSA), and motor imagery (MI). Results: OSA modulation was shown to provide comparable control to conventional MI modulation in both 1-D and 2-D tasks. Furthermore, this paper provides evidence for the functional independence of traditional MI and OSA, as well as an investigation into the simultaneous use of both. Using this newly proposed BCI paradigm, 16 participants successfully completed a 3-D eight-target control task. Nine of these subjects further demonstrated robust 3-D control in a 12-target task, significantly outperforming the information transfer rate achieved in the 1-D and 2-D control tasks (29.7 ± 1.6 b/min). Conclusion: These results strongly support the hypothesis that noninvasive EEG-based BCI can provide robust 3-D control through endogenous neural modulation in broader populations with limited training. Significance: Through the combination of the two strategies (MI and OSA), a substantial portion of the recruited subjects were capable of robustly controlling a virtual cursor in 3-D space. The proposed novel approach could broaden the dimensionality of BCI control and shorten the training time.

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Journal ArticleDOI

Brain-Computer Interface: Advancement and Challenges.

TL;DR: In this paper, a comprehensive overview of the brain-computer interface (BCI) domain is presented, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers.
Journal ArticleDOI

Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs.

TL;DR: Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance and indicate that the proposed approach is expected to be practical in MI-based BCIs.
Journal ArticleDOI

Evaluation of Eye-Movement Metrics ina Software DebbugingTask using GP3 Eye Tracker

TL;DR: The applicability of eye movement tracking systems in respect of a programming task is examined, in which during the exploration and correction of the errors of an incorrectly functioning algorithm, the eye movement parameters are observed, recorded and evaluated.
Posted ContentDOI

Benefits of Deep Learning Classification of Continuous Noninvasive Brain-Computer Interface Control

TL;DR: Improvements in classification accuracy were found to negatively correlate with initial online BCI performance, suggesting deep learning methods preferentially benefit BCI participants who need it most, and suggest that a variety of neural biomarkers useful for BCI, including those outside the motor cortex, can be detected through deepLearning methods.
References
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Journal ArticleDOI

The Attention System of the Human Brain

TL;DR: Illustration de trois fonctions principales qui sont predominantes dans l'etude de l'intervention de l'sattention dans les processus cognitifs: 1) orientation vers des evenements sensoriels; 2) detection des signaux par processus focal; 3) maintenir la vigilance en etat d'alerte
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.
Journal ArticleDOI

Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

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

High-performance neuroprosthetic control by an individual with tetraplegia

TL;DR: With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.
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.
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