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Stacked Autoencoders for the P300 Component Detection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43931861" target="_blank" >RIV/49777513:23520/17:43931861 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.3389/fnins.2017.00302" target="_blank" >http://dx.doi.org/10.3389/fnins.2017.00302</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3389/fnins.2017.00302" target="_blank" >10.3389/fnins.2017.00302</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Stacked Autoencoders for the P300 Component Detection

  • Original language description

    Using a combination of unsupervised pre-training and subsequent fine-tuning, deep neural networks have become one of the most reliable classification methods. The aim of the experiments subsequently presented was to verify if deep learning-based models can also perform well for single trial P300 classification with possible application to P300-based brain-computer interfaces. The P300 data used were recorded in the EEG/ERP laboratory at the Department of Computer Science and Engineering, University of West Bohemia, and are publicly available. Stacked autoencoders were implemented and compared with some of the currently most reliable state-of-the-art methods, such as LDA and multi-layer perceptron. The parameters of stacked autoencoders were optimized empirically. Subsequently, fine-tuning using backpropagation was performed. The architecture of the neural network was 209-130-100-50-20-2. The classifiers were trained on a dataset merged from four subjects and subsequently tested on different 11 subjects without further training. The trained SAE achieved 69.2% accuracy that was higher (p &lt; 0.01) than the accuracy of MLP (64.9%) and LDA (65.9%). The recall of 58.8% was slightly higher when compared with MLP (56.2%) and LDA (58.4%).

  • 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)

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Frontiers in Neuroscience

  • ISSN

    1662-453X

  • e-ISSN

  • Volume of the periodical

    11

  • Issue of the periodical within the volume

    302

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    9

  • Pages from-to

    1-9

  • UT code for WoS article

    000406529900001

  • EID of the result in the Scopus database

    2-s2.0-85020069785