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2024 | OriginalPaper | Chapter

8. Biomedizinische Signalverarbeitung und künstliche Intelligenz in EOG-Signalen

Authors : Alberto López, Francisco Ferrero

Published in: Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML

Publisher: Springer International Publishing

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Zusammenfassung

Die Elektrookulographie ist eine Technik, die Augenbewegungen erkennt und analysiert, basierend auf elektrischen Potenzialen, die mit um die Augen platzierten Elektroden aufgezeichnet werden. Das aufgezeichnete elektrische Signal wird als Elektrookulogramm (EOG) bezeichnet und kann als alternative Eingabe für medizinische Systeme und Mensch-Computer-Schnittstellensysteme verwendet werden. Um ein auf Augenbewegungen basierendes System zu implementieren, werden mindestens vier Hauptstufen benötigt: Signalentstörung, Merkmalsextraktion, Signal-Klassifizierung und Entscheidungsfindung. Die erste Stufe nach der EOG-Signalerfassung ist die Signalentstörung, die Rauschen unterdrückt, das nicht durch die analogen Filter entfernt werden konnte. Bei dieser Aufgabe müssen die Steigung der Signalränder sowie die Amplituden des Signals zur Unterscheidung zwischen verschiedenen Augenbewegungen erhalten bleiben. Nach der Entstörung ist die zweite Aufgabe die Extraktion der Merkmale des EOG-Signals, hauptsächlich basierend auf der Erkennung von Sakkaden, Fixationen und Blinzeln. Die nächste Stufe ist die automatische Identifizierung von Augenbewegungen. Diese Aufgabe, die Signal-Klassifizierung genannt wird, ist für die Erzeugung genauer Befehle, insbesondere in Echtzeitanwendungen, unerlässlich. Diese Klassifizierung wird hauptsächlich mit einer Kombination von Algorithmen in der künstlichen Intelligenz (KI) durchgeführt. Diese Arten von Algorithmen sind am besten geeignet für adaptive Systeme, die eine Echtzeit-Entscheidungsfindung, unterstützt durch KI-Techniken, erfordern. In einigen Anwendungen werden auch EOG-Modellierung und -Kompression als zusätzliche Signalverarbeitungsstufe angewendet.

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previous chapter Überblick über die Klassifizierung von EEG-Signalen mit maschinellem Lernen und Deep-Learning-Techniken

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Title: Biomedizinische Signalverarbeitung und künstliche Intelligenz in EOG-Signalen
Authors: Alberto López
Francisco Ferrero
Publisher: Springer International Publishing
Book: Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML
Print ISBN: 978-3-031-52855-2

Electronic ISBN: 978-3-031-52856-9

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-52856-9_8

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