Spiking Neural Networks for Classification of Brain-Computer Interface and Image Data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Spiking Neural Networks for Classification of Brain-Computer Interface and Image Data
Original language description
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.
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021)
ISBN
978-1-66540-126-5
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
3624-3629
Publisher name
IEEE
Place of publication
Piscataway
Event location
online
Event date
Dec 9, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—