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Gehirn-Computer-Schnittstelle (BCI), basierend auf der EEG-Signalzerlegung, Schmetterlingsoptimierung und maschinellem Lernen

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Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML

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Zusammenfassung

Die Gehirn-Computer-Schnittstelle (BCI) ist eine Technologie, die Menschen mit Behinderungen hilft, Hilfsgeräte zu bedienen, indem sie neuromuskuläre Kanäle umgeht. Diese Studie zielt darauf ab, die Elektroenzephalographie (EEG) Signale zu verarbeiten und diese Signale dann durch Analyse und Kategorisierung mit Maschinenlernalgorithmen in Befehle zu übersetzen. Die Ergebnisse können weiterhin zur Steuerung eines Hilfsgeräts verwendet werden. Die Bedeutung dieses Projekts liegt in der Unterstützung von Menschen mit schweren motorischen Beeinträchtigungen, Lähmungen oder denen, die ihre Gliedmaßen verloren haben, um unabhängig und selbstbewusst zu sein, indem sie ihre Umgebung kontrollieren und ihnen alternative Kommunikationswege bieten. Die erworbenen EEG-Signale werden digital mit einem Tiefpass gefiltert und dezimiert. Anschließend wird die Wavelet-Zerlegung zur Signalanalyse verwendet. Die Merkmale werden aus den erhaltenen Unterbändern abgebaut. Die Dimension des extrahierten Merkmalsatzes wird durch Verwendung des Butterfly-Optimierungsalgorithmus reduziert. Der ausgewählte Merkmalsatz wird dann von den Klassifikatoren verarbeitet. Die Leistung des k-Nearest Neighbor, der Support Vector Machine und des Artificial Neural Network wird für die Kategorisierung von motorischen Imagery-Aufgaben durch Verarbeitung des ausgewählten Merkmalsatzes verglichen. Die vorgeschlagene Methode sichert eine höchste Genauigkeitsbewertung von 83,7 % für den Fall des k-Nearest Neighbor-Klassifikators.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi-Arabien

    Mawadda Alghamdi, Saeed Mian Qaisar, Shahad Bawazeer & Faya Saifuddin

  2. Department of Urology, Sahiwal Teaching Hospital, Sahiwal, Pakistan

    Majed Saeed

Authors
  1. Mawadda Alghamdi
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  2. Saeed Mian Qaisar
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  3. Shahad Bawazeer
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  4. Faya Saifuddin
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  5. Majed Saeed
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Correspondence to Saeed Mian Qaisar .

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Editors and Affiliations

  1. Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia

    Saeed Mian Qaisar

  2. Department of Electronic Engineering Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Malaysia

    Humaira Nisar

  3. Institute of Biomedicine Faculty of Medicine, University of Turku, Turku, Finland

    Abdulhamit Subasi

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Alghamdi, M., Mian Qaisar, S., Bawazeer, S., Saifuddin, F., Saeed, M. (2024). Gehirn-Computer-Schnittstelle (BCI), basierend auf der EEG-Signalzerlegung, Schmetterlingsoptimierung und maschinellem Lernen. In: Qaisar, S.M., Nisar, H., Subasi, A. (eds) Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML. Springer Vieweg, Cham. https://doi.org/10.1007/978-3-031-52856-9_4

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