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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • 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

  • e-ISSN

  • Number of pages

    9

  • Pages from-to

    158-166

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Hoboken &amp; New Jersey - USA

  • Event date

    Aug 1, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article