"You Have Reached Your Destination": A Single Trial EEG Classification Study.

BCI EEG P300 classification human machine interaction navigation neurophysiology target recognition

Journal

Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481

Informations de publication

Date de publication:
2020
Historique:
received: 03 09 2019
accepted: 16 01 2020
entrez: 3 3 2020
pubmed: 3 3 2020
medline: 3 3 2020
Statut: epublish

Résumé

Studies have established that it is possible to differentiate between the brain's responses to observing correct and incorrect movements in navigation tasks. Furthermore, these classifications can be used as feedback for a learning-based BCI, to allow real or virtual robots to find quasi-optimal routes to a target. However, when navigating it is important not only to know we are moving in the right direction toward a target, but also to know when we have reached it. We asked participants to observe a virtual robot performing a 1-dimensional navigation task. We recorded EEG and then performed neurophysiological analysis on the responses to two classes of correct movements: those that moved closer to the target but did not reach it, and those that did reach the target. Further, we used a stepwise linear classifier on time-domain features to differentiate the classes on a single-trial basis. A second data set was also used to further test this single-trial classification. We found that the amplitude of the P300 was significantly greater in cases where the movement reached the target. Interestingly, we were able to classify the EEG signals evoked when observing the two classes of correct movements against each other with mean overall accuracy of 66.5 and 68.0% for the two data sets, with greater than chance levels of accuracy achieved for all participants. As a proof of concept, we have shown that it is possible to classify the EEG responses in observing these different correct movements against each other using single-trial EEG. This could be used as part of a learning-based BCI and opens a new door toward a more autonomous BCI navigation system.

Identifiants

pubmed: 32116513
doi: 10.3389/fnins.2020.00066
pmc: PMC7027274
doi:

Types de publication

Journal Article

Langues

eng

Pagination

66

Informations de copyright

Copyright © 2020 Wirth, Toth and Arvaneh.

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Auteurs

Christopher Wirth (C)

Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield, United Kingdom.

Jake Toth (J)

Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield, United Kingdom.

Mahnaz Arvaneh (M)

Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield, United Kingdom.

Classifications MeSH