Spiking Neural Networks for Classification of Brain-Computer Interface and Image Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43963615" target="_blank" >RIV/49777513:23520/21:43963615 - isvavai.cz</a>

Výsledek na webu

<a href="http://jen" target="_blank" >http://jen</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM52615.2021.9669864" target="_blank" >10.1109/BIBM52615.2021.9669864</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spiking Neural Networks for Classification of Brain-Computer Interface and Image Data

Popis výsledku v původním jazyce

Spiking neural networks are a promising concept not only in terms of better simulation of biological neural networks but also in terms of overcoming the current disadvantages of artificial neural networks, such as high energy consumption or slow response time. The paper focuses on the potential benefits of spiking neural networks in the classification of event-related components processed in many traditional brain-computer interface experiments. Experiments with various spiking network architectures and optimization approaches over specific brain-computer interface and image datasets are presented, and their results are provided and discussed. The best accuracy achieved was 64.86% for the event-related component dataset and 97.09% for the image dataset.

Název v anglickém jazyce

Spiking Neural Networks for Classification of Brain-Computer Interface and Image Data

Popis výsledku anglicky

Spiking neural networks are a promising concept not only in terms of better simulation of biological neural networks but also in terms of overcoming the current disadvantages of artificial neural networks, such as high energy consumption or slow response time. The paper focuses on the potential benefits of spiking neural networks in the classification of event-related components processed in many traditional brain-computer interface experiments. Experiments with various spiking network architectures and optimization approaches over specific brain-computer interface and image datasets are presented, and their results are provided and discussed. The best accuracy achieved was 64.86% for the event-related component dataset and 97.09% for the image dataset.

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021)

ISBN

978-1-66540-126-5

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

3624-3629

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

online

Datum konání akce

9. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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