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Classifying the EEG Signal through Stimulus of Motor Movement Using New Type of Wavelet

IAES International Journal of Artificial Intelligence · Vol. 1 No. 3 (2012) · pp. 139–148

ISSN
2252-8938, 2089-4872
Published
2012-09-13
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Brain Computer Interface (BCI) refers to a system designed to translate the brain signal in controlling a computer application. The most widely used brain signal is electroencephalograph (EEG) for using the non-invasive method, and having a quite good resolution and relatively affordable equipments. This research purposively is to obtain the characteristics of EEG signals using the motor movement of “turn right” and “turn left” that is by moving the simulation of steering wheel. The characteristic of signal obtained is subsequently used as a reference to create a new type of wavelet for classification. The signal processing, including a 4 – 20 Hz bandpass filter, signal segmentation in 1 to 2 seconds after stimuli and signal correlation, is used to obtain the characteristic of EEG signal; namely Event–Related Synchronization /Desynchronization (ERS/ERD). The result of test data classification to two new types of wavelet shows that each volunteer has a higher correlation value towards the new type of wavelet that has been designed with various wavelet scales for each individuals.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.843

Collaboration network of the authors

16 authors · 29 co-authorship links · 15 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Poltekkes Kemenkes Surabaya
  • University of Technology Malaysia
  • University of Brawijaya
  • Muhammadiyah University of Surakarta
  • Sultan Agung Islamic University
  • Universitas Airlangga
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fakhriy HarioUniversitas Gadjah Mada2
I Wayan MustikaUniversitas Gadjah Mada2
Sevia Mahdaliza IdrusUniversity of Technology Malaysia2
Sholeh Hadi P.University of Brawijaya2
Oyas WahyunggoroUniversitas Gadjah Mada2
Muhammad KusbanMuhammadiyah University of Surakarta2
Sri Arttini Dwi PrasetyowatiSultan Agung Islamic University1
Umaimah Mitsalia Ummi SalwaUniversitas Airlangga1
Triwiyanto TriwiyantoPoltekkes Kemenkes Surabaya1
Tri Bowo IndartoPoltekkes Kemenkes Surabaya1
Zaenuri ZaenuriState University of Semarang1
Nuriana Rachmani DewiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Classifying the EEG Signal through Stimulus of Motor Movement Using New Type of Wavelet</dc:title>
	<dc:creator>Yulianto, Endro</dc:creator>
	<dc:creator>Susanto, Adhi</dc:creator>
	<dc:creator>Sri Widodo, Thomas</dc:creator>
	<dc:creator>Wibowo, Samekto</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:description xml:lang="en-US">Brain Computer Interface (BCI) refers to a system designed to translate the brain signal in controlling a computer application.  The most widely used brain signal is electroencephalograph (EEG) for using the non-invasive method, and having a quite good resolution and relatively affordable equipments. This research purposively is to obtain the characteristics of EEG signals using the motor movement of “turn right” and “turn left” that is by moving the simulation of steering wheel. The characteristic of signal obtained is subsequently used as a reference to create a new type of wavelet for classification. The signal processing, including a 4 – 20 Hz bandpass filter, signal segmentation in 1 to 2 seconds after stimuli and signal correlation,  is used to obtain the characteristic of EEG signal; namely Event–Related Synchronization /Desynchronization (ERS/ERD). The result of test data classification to two new types of wavelet shows that each volunteer has a higher correlation value towards the new type of wavelet that has been designed with various wavelet scales for each individuals.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.843</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2012-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/1283</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 1, No 3: September 2012; 139-148</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v1.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/1283/1181</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2012 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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