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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

—

Svazek periodika

58

Číslo periodika v rámci svazku

APR 2020

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

1-7

Kód UT WoS článku

000518869700013

EID výsledku v databázi Scopus

2-s2.0-85077454141