Classification of Hand Movement in EEG using ERD/ERS and Multilayer Perceptron

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43960748" target="_blank" >RIV/49777513:23520/20:43960748 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0009167007130717" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0009167007130717</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0009167007130717" target="_blank" >10.5220/0009167007130717</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Hand Movement in EEG using ERD/ERS and Multilayer Perceptron

Popis výsledku v původním jazyce

Continuous EEG activity in the measured subjects includes different patterns depending on what activity the subject performed. ERD and ERS are examples of such patterns related to movement, for example of a hand, finger or foot. This article deals with the detection of motion based on the ERD/ERS patterns. By linking ERD/ERS, feature vectors which are later classified by neural network are created. The resulting neural network consists of one input and one output layer and two hidden layers. The first hidden layer contains 3,000 neurons and the second one 1,500 neurons. A training set of feature vectors is used for the training of this neural network and the back-propagation algorithm is used for the subsequent adjustment of the weights. With this setting and training, the neural network is able to classify motion in an EEG record with an average accuracy of 79.92%.

Název v anglickém jazyce

Classification of Hand Movement in EEG using ERD/ERS and Multilayer Perceptron

Popis výsledku anglicky

Continuous EEG activity in the measured subjects includes different patterns depending on what activity the subject performed. ERD and ERS are examples of such patterns related to movement, for example of a hand, finger or foot. This article deals with the detection of motion based on the ERD/ERS patterns. By linking ERD/ERS, feature vectors which are later classified by neural network are created. The resulting neural network consists of one input and one output layer and two hidden layers. The first hidden layer contains 3,000 neurons and the second one 1,500 neurons. A training set of feature vectors is used for the training of this neural network and the back-propagation algorithm is used for the subsequent adjustment of the weights. With this setting and training, the neural network is able to classify motion in an EEG record with an average accuracy of 79.92%.

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

BIOSTEC 2020

ISBN

978-989-758-398-8

ISSN

2184-4305

e-ISSN

—

Počet stran výsledku

5

Strana od-do

713-717

Název nakladatele

SciTiPress

Místo vydání

Setúbal

Místo konání akce

Valletta Malta

Datum konání akce

24. 2. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000571479400081