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Evaluation of convolutional neural networks using a large multi-subject P300 dataset

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43957272" target="_blank" >RIV/49777513:23520/20:43957272 - isvavai.cz</a>

  • Result on the web

    <a href="http://VarekaCNN_paper.pdf" target="_blank" >http://VarekaCNN_paper.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.bspc.2019.101837" target="_blank" >10.1016/j.bspc.2019.101837</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Evaluation of convolutional neural networks using a large multi-subject P300 dataset

  • Original language description

    Deep neural networks (DNN) have been studied in various machine learning areas. For example, event-related potential (ERP) signal classification is a highly complex task potentially suitable for DNN as signal-to-noise ratio is low, and underlying spatial and temporal patterns display a large intra- and intersubject variability. Convolutional neural networks (CNN) have been compared with baseline traditional models, i.e. linear discriminant analysis (LDA) and support vector machines (SVM) for single trial classification using a large multi-subject publicly available P300 dataset of school-age children (138 males and 112 females). For single trial classification, classification accuracy stayed between 62% and 64% for all tested classification models. When applying the trained classification models to averaged trials, accuracy increased to 76–79% without significant differences among classification models. CNN did not prove superior to baseline for the tested dataset. Comparison with related literature, limitations and future directions are discussed.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

    <a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Biomedical Signal Processing and Control

  • ISSN

    1746-8094

  • e-ISSN

  • Volume of the periodical

    58

  • Issue of the periodical within the volume

    APR 2020

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    7

  • Pages from-to

    1-7

  • UT code for WoS article

    000518869700013

  • EID of the result in the Scopus database

    2-s2.0-85077454141