Intracranial Error Detection via Deep Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11130%2F18%3A10392819" target="_blank" >RIV/00216208:11130/18:10392819 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00064203:_____/18:10392819

Výsledek na webu

<a href="https://doi.org/10.1109/SMC.2018.00106" target="_blank" >https://doi.org/10.1109/SMC.2018.00106</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC.2018.00106" target="_blank" >10.1109/SMC.2018.00106</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intracranial Error Detection via Deep Learning

Popis výsledku v původním jazyce

Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural networks (CNNs) to classify and characterize the error-related brain response as measured in 24 intracranial EEG recordings. Decoding accuracies of CNNs were significantly higher than those of a regularized linear discriminant analysis. Using time-resolved deep decoding, it was possible to classify errors in various regions in the human brain, and further to decode errors over 200 ms before the actual erroneous button press, e.g., in the precentral gyrus. Moreover, deeper networks performed better than shallower networks in distinguishing correct from error trials in all-channel decoding. In single recordings, up to 100 % decoding accuracy was achieved. Visualization of the networks&apos; learned features indicated that multivariate decoding on an ensemble of channels yields related, albeit non-redundant information compared to single-channel decoding. In summary, here we show the usefulness of deep learning for both intracranial error decoding and mapping of the spatio-temporal structure of the human error processing network.

Název v anglickém jazyce

Intracranial Error Detection via Deep Learning

Popis výsledku anglicky

Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural networks (CNNs) to classify and characterize the error-related brain response as measured in 24 intracranial EEG recordings. Decoding accuracies of CNNs were significantly higher than those of a regularized linear discriminant analysis. Using time-resolved deep decoding, it was possible to classify errors in various regions in the human brain, and further to decode errors over 200 ms before the actual erroneous button press, e.g., in the precentral gyrus. Moreover, deeper networks performed better than shallower networks in distinguishing correct from error trials in all-channel decoding. In single recordings, up to 100 % decoding accuracy was achieved. Visualization of the networks&apos; learned features indicated that multivariate decoding on an ensemble of channels yields related, albeit non-redundant information compared to single-channel decoding. In summary, here we show the usefulness of deep learning for both intracranial error decoding and mapping of the spatio-temporal structure of the human error processing network.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

ISBN

978-1-5386-6650-0

ISSN

1062-922X

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

568-575

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

IEEE Syst Man &amp; Cybernet Soc

Datum konání akce

7. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000459884800097