| Title: | Optimal sensor set for decoding motor imagery from EEG |
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| 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) |
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| Files: |  URL - Source URL, visit https://doi.org/10.3390/app13074438
 PDF - Presentation file, download (670,67 KB)
MD5: 7D3288A00BE427365C887AB0134A46F8
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  ZRS Koper - Science and Research Centre Koper
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| 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. |
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| Keywords: | brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning |
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| Article acceptance date: | 29.03.2023 |
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| Publication date: | 31.03.2023 |
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| Year of publishing: | 2023 |
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| Number of pages: | 15 str. |
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| Numbering: | Vol. 13, iss. 7, [article no.] 4438 |
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| PID: | 20.500.12556/DiRROS-16427  |
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| UDC: | 616.8-073.97:796.012 |
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| ISSN on article: | 2076-3417 |
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| DOI: | 10.3390/app13074438 |
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| COBISS.SI-ID: | 147627523 |
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| Copyright: | © 2023 by the authors. |
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| 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;
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| Publication date in DiRROS: | 03.04.2023 |
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| Views: | 467 |
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| Downloads: | 191 |
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