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Extraction of the Major Features of Brain Signals using Intelligent Networks

Journal of ICT Research and Applications (JICTRA) · Vol. 15 No. 1 (2021) · pp. 71–88

DOI
10.5614/itbj.ict.res.appl.2021.15.1.5
ISSN
2338-5499, 2337-5787
Published
2021-07-05
Publisher
DRI - ITB
Language
eng

Abstract

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.

Keywords

Collaboration network of the authors

2 authors · 1 co-authorship link

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  • Islamic Azad University South Tehran Branch
  • Different organisations
  • Same organisation
  • Affiliation unknown
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Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Extraction of the Major Features of Brain Signals using Intelligent Networks</dc:title>
	<dc:creator>Salarian, Shirin</dc:creator>
	<dc:creator>Shahabi, Amir Shahab</dc:creator>
	<dc:subject xml:lang="en-US">brain-computer interface</dc:subject>
	<dc:subject xml:lang="en-US">EEG signal</dc:subject>
	<dc:subject xml:lang="en-US">P300 component</dc:subject>
	<dc:subject xml:lang="en-US">recurrent neural network</dc:subject>
	<dc:subject xml:lang="en-US">twin support vector machine</dc:subject>
	<dc:description xml:lang="en-US">The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-07-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/13854</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.15.1.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 15 No. 1 (2021); 71-88</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2021.15.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/13854/5287</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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