Issue |
MATEC Web Conf.
Volume 299, 2019
Modern Technologies in Manufacturing (MTeM 2019)
|
|
---|---|---|
Article Number | 02004 | |
Number of page(s) | 10 | |
Section | Automation of Manufacturing Systems and Assembly | |
DOI | https://doi.org/10.1051/matecconf/201929902004 | |
Published online | 02 December 2019 |
Taking brain-computer-interfacing one step further: a portable, wireless system coupled with online linear discriminant analysis for the detection of error-related potentials
1
Faculty of Electrical Engineering, Technical University of Cluj-Napoca,
Cluj-Napoca,
Romania
2
Department of Neurology, Klinikum rechts der Isar, Technical University of Munich,
München,
Germany
* Corresponding author: dorinaancau@yahoo.com
Recent years have witnessed extensive developments of computer science applications in medicine - assistive technologies. Among them, the concept of Brain-Computer-Interfaces, facilitating direct communication between brain and computer, has inspired numerous practical ideas on controlling an external device via neural signals. The perception of an error made by oneself, another human or a machine, triggers an error-related potential, which has already been exploited as a binary correction readout for decisions made by Brain-ComputerInterfaces. Our approach takes advantage of this technique, while taking it one step further regarding portability by using an affordable, robust and wireless headset, the Emotiv EPOC+, to recognize error-related potentials in electroencephalograms of subjects performing various on-site, dynamic tasks. We also introduce a straightforward linear-discriminant analysis classifier that extends the range of detection from offline, post-hoc analysis, to online, within-trial recordings, an essential condition towards blending machine-performed tasks with human-generated thought processes in everyday life.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.