About: Similarity analysis of EEG data based on self organizing map neural network

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://advances.utc.sk/index.php/AEEE/article/view/1171
Description	The Electroencephalography (EEG) is the recording of electrical activity along the scalp. This recorded data are very complex. EEG has a big role in several applications such as in the diagnosis of human brain diseases and epilepsy. Also, we can use the EEG signals to control an external device via Brain Computer Interface (BCI) by our mind. There are many algorithms to analyse the recorded EEG data, but it still remains one of the big challenges in the world. In this article, we extended our previous proposed method. Our extended method uses Self-organizing Map (SOM) as an EEG data classifier. The proposed method we can divide in following steps: capturing EEG raw data from the sensors, applying filters on this data, we will use the frequencies in the range from 0.5 Hz to 60 Hz, smoothing the data with 15-th order of Polynomial Curve Fitting, converting filtered data into text using Turtle Graphic, Lempel-Ziv complexity for measuring similarity between two EEG data trials and Self-Organizing Map Neural Network as a final classifiers. The experiment results show that our model is able to detect up to 96% finger movements correctly. The Electroencephalography (EEG) is the recording of electrical activity along the scalp. This recorded data are very complex. EEG has a big role in several applications such as in the diagnosis of human brain diseases and epilepsy. Also, we can use the EEG signals to control an external device via Brain Computer Interface (BCI) by our mind. There are many algorithms to analyse the recorded EEG data, but it still remains one of the big challenges in the world. In this article, we extended our previous proposed method. Our extended method uses Self-organizing Map (SOM) as an EEG data classifier. The proposed method we can divide in following steps: capturing EEG raw data from the sensors, applying filters on this data, we will use the frequencies in the range from 0.5 Hz to 60 Hz, smoothing the data with 15-th order of Polynomial Curve Fitting, converting filtered data into text using Turtle Graphic, Lempel-Ziv complexity for measuring similarity between two EEG data trials and Self-Organizing Map Neural Network as a final classifiers. The experiment results show that our model is able to detect up to 96% finger movements correctly. (en)
Title	Similarity analysis of EEG data based on self organizing map neural network Similarity analysis of EEG data based on self organizing map neural network (en)
skos:prefLabel	Similarity analysis of EEG data based on self organizing map neural network Similarity analysis of EEG data based on self organizing map neural network (en)
skos:notation	RIV/61989100:27740/14:86092915!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(ED1.1.00/02.0070), S
http://linked.open...iv/cisloPeriodika	5
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Penhaker, Marek Snášel, Václav Prílepok, Michal
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / IT4Innovations
http://linked.open...dnocenehoVysledku	44873
http://linked.open...ai/riv/idVysledku	RIV/61989100:27740/14:86092915
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Unsupervised learning; SOM; Polynomial curve fitting; Electroencephalograph; EEG data (en)
http://linked.open.../riv/klicoveSlovo	SOM Electroencephalograph Polynomial curve fitting Unsupervised learning EEG data
http://linked.open...odStatuVydavatele	CZ - Česká republika
http://linked.open...ontrolniKodProRIV	[10189F8C0220]
http://linked.open...i/riv/nazevZdroje	Advances in Electrical and Electronic Engineering
http://linked.open...in/vavai/riv/obor	JC
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	IT4Innovations Centre of Excellence
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	12
http://linked.open...iv/tvurceVysledku	Penhaker, Marek Snášel, Václav Prílepok, Michal Jahan, Ibrahim Salem
issn	1336-1376
number of pages	10 (xsd:int)
http://bibframe.org/vocab/doi	10.15598/aeee.v12i5.1171
http://localhost/t...ganizacniJednotka	27740

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