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Title:Optimal sensor set for decoding motor imagery from EEG
Authors:ID Dillen, Arnau (Author)
ID Ghaffari, Fakhreddine (Author)
ID Romain, Olivier (Author)
ID Vanderborght, Bram (Author)
ID Marušič, Uroš (Author)
ID Grosprêtre, Sidney (Author)
ID Nowé, Ann (Author)
ID Meeusen, Romain (Author)
ID De Pauw, Kevin (Author)
Files:URL URL - Source URL, visit https://doi.org/10.3390/app13074438
 
.pdf PDF - Presentation file, download (670,67 KB)
MD5: 7D3288A00BE427365C887AB0134A46F8
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo ZRS Koper - Science and Research Centre Koper
Abstract:Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.
Keywords:brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning
Article acceptance date:29.03.2023
Publication date:31.03.2023
Year of publishing:2023
Number of pages:15 str.
Numbering:Vol. 13, iss. 7, [article no.] 4438
PID:20.500.12556/DiRROS-16427 New window
UDC:616.8-073.97:796.012
ISSN on article:2076-3417
DOI:10.3390/app13074438 New window
COBISS.SI-ID:147627523 New window
Copyright:© 2023 by the authors.
Note:Nasl. z nasl. zaslona; Soavtorji: Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uros Marusic, Sidney Grosprêtre, Ann Nowé, Romain Meeusen, Kevin De Pauw; Opis vira z dne 3. 4. 2023;
Publication date in DiRROS:03.04.2023
Views:612
Downloads:273
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Record is a part of a journal

Title:Applied sciences
Shortened title:Appl. sci.
Publisher:MDPI
ISSN:2076-3417
COBISS.SI-ID:522979353 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:31.03.2023

Secondary language

Language:Slovenian
Keywords:vmesnik možgani-računalniki, gibalne predstave


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