Biomedical Signal Processing and Artificial Intelligence in EOG Signals

  1. López, Alberto 1
  2. Ferrero, Francisco 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Libro:
Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning

ISBN: 9783031232381,  9783031232398

Año de publicación: 2023

Páginas: 185-206

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-031-23239-8_8 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Electrooculography is a technique that detects and analyses eye movement based on electrical potentials recorded using electrodes placed around the eyes. The electrical signal recorded is named electrooculogram (EOG) and can be used as an alternative input for medical and human-computer interface systems. To implement an eye movement-based system, at least four main stages will be required: signal denoising, feature extraction, signal classification and decision-making. The first one after the EOG signal acquisition is signal denoising, which suppresses noise that could not be removed by the analogue filters. In this task, the slope of the signal edges, as well as the amplitudes of the signal to distinguish between different eye movements, must be preserved. After denoising, the second task is to extract the features of the EOG signal based mainly on the detection of saccades, fixations, and blinks. The next stage is the automatic identification of eye movements. This task, called signal classification, is essential for generating accurate commands, especially in real-time applications. This classification is carried out mainly using a combination of algorithms in artificial intelligence (AI). These types of algorithms are the most suitable for adaptive systems that require real-time decision-making supported by AI techniques. In some applications, EOG modelling, and compression are also applied as an additional signal processing stage.

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