Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969261" target="_blank" >RIV/49777513:23520/23:43969261 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-43075-6_14" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-43075-6_14</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-43075-6_14" target="_blank" >10.1007/978-3-031-43075-6_14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset

Popis výsledku v původním jazyce

Event-related potential signal classification is a really difficult challenge due to the low signal-to-noise ratio. Deep neural networks (DNN), which have been employed in different machine learning areas, are suitable for this type of classification. UNet (a convolutional neural network) is a classification algorithm proposed to improve the classification accuracy of P300 electroencephalogram (EEG) signals in a non-invasive brain-computer interface. The proposed UNet classification accuracy and precision were 64.5% for single-trial classification using a large P300 dataset of school-aged children, including 138 males and 112 females. We compare our results with the related literature and discuss limitations and future directions. Our proposed method performed better than traditional methods.

Název v anglickém jazyce

Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset

Popis výsledku anglicky

Event-related potential signal classification is a really difficult challenge due to the low signal-to-noise ratio. Deep neural networks (DNN), which have been employed in different machine learning areas, are suitable for this type of classification. UNet (a convolutional neural network) is a classification algorithm proposed to improve the classification accuracy of P300 electroencephalogram (EEG) signals in a non-invasive brain-computer interface. The proposed UNet classification accuracy and precision were 64.5% for single-trial classification using a large P300 dataset of school-aged children, including 138 males and 112 females. We compare our results with the related literature and discuss limitations and future directions. Our proposed method performed better than traditional methods.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Brain Informatics

ISBN

978-3-031-43074-9

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

158-166

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Hoboken &amp; New Jersey - USA

Datum konání akce

1. 8. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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