Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Brain Informatics
ISBN
978-3-031-43074-9
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
158-166
Publisher name
Springer
Place of publication
Cham
Event location
Hoboken & New Jersey - USA
Event date
Aug 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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