Advanced Analysis of Electro-Oculographic Signal using Deep Neural Networks for Safety Purposes in Automation and Production Systems

2023 15th International Conference on Computer and Automation Engineering (ICCAE) Pub Date : 2023-03-03 DOI:10.1109/ICCAE56788.2023.10111377

I. Kuric, Vladimír Stenchlák, I. Zajačko, M. Bohušík, D. Więcek

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Abstract

This article deals with the problematics of obtaining and processing the EOG signals. This research describes the ways in which it is possible to measure and evaluate the measured bio-electrical signals of brain and muscle activity in more detail. The work also describes the analysis of the current state of this problematics and individual models of artificial intelligence are also described in the work - artificial neural networks that can be used for such advanced analysis. The experimental part of this work is performed in the MATLAB environment using the Deep Neural Network Toolbox. The experimental part of the work is focused on the analysis and processing of bio-electrical activity eye-motor muscles in the area of eye-tracking with the help of the OLIMEX EEG-SMT device. The information provided in this research can be used for improving safety and reliability and elimination of potential risks connected to an epilepsy disease and epilepsy seizures in automated production systems.

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自动化和生产系统中基于安全目的的深度神经网络眼电信号高级分析

本文讨论了测井信号的获取和处理问题。这项研究详细描述了测量和评估大脑和肌肉活动的生物电信号的方法。该工作还描述了对该问题的当前状态的分析，并且在工作中还描述了人工智能的单个模型-可用于此类高级分析的人工神经网络。本工作的实验部分是在MATLAB环境下使用深度神经网络工具箱进行的。实验部分的工作重点是在OLIMEX EEG-SMT设备的帮助下，分析和处理眼动肌肉在眼动追踪领域的生物电活动。本研究提供的信息可用于提高自动化生产系统的安全性和可靠性，并消除与癫痫疾病和癫痫发作有关的潜在风险。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2023 15th International Conference on Computer and Automation Engineering (ICCAE)

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