| Naslov: | Optimal sensor set for decoding motor imagery from EEG |
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| Avtorji: | ID Dillen, Arnau (Avtor)
ID Ghaffari, Fakhreddine (Avtor)
ID Romain, Olivier (Avtor)
ID Vanderborght, Bram (Avtor)
ID Marušič, Uroš (Avtor)
ID Grosprêtre, Sidney (Avtor)
ID Nowé, Ann (Avtor)
ID Meeusen, Romain (Avtor)
ID De Pauw, Kevin (Avtor) |
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| Datoteke: |  URL - Izvorni URL, za dostop obiščite https://doi.org/10.3390/app13074438
 PDF - Predstavitvena datoteka, prenos (670,67 KB)
MD5: 7D3288A00BE427365C887AB0134A46F8
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| Jezik: | Angleški jezik |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: |  ZRS Koper - Znanstveno-raziskovalno središče Koper / Centro di Ricerche Scientifiche Capodistria
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| Povzetek: | 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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| Ključne besede: | brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning |
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| Datum sprejetja članka: | 29.03.2023 |
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| Datum objave: | 31.03.2023 |
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| Leto izida: | 2023 |
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| Št. strani: | 15 str. |
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| Številčenje: | Vol. 13, iss. 7, [article no.] 4438 |
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| PID: | 20.500.12556/DiRROS-16427  |
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| UDK: | 616.8-073.97:796.012 |
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| ISSN pri članku: | 2076-3417 |
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| DOI: | 10.3390/app13074438 |
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| COBISS.SI-ID: | 147627523 |
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| Avtorske pravice: | © 2023 by the authors. |
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| Opomba: | 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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| Datum objave v DiRROS: | 03.04.2023 |
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| Število ogledov: | 842 |
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| Število prenosov: | 395 |
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| Metapodatki: |    |
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