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Attention and Meditation Quantification Using Neural Networks

Authors:
Anca O. Muresan
Felix G. Hamza-Lup

Keywords: brain-computer interface; attention quantification

Abstract:
The advancements in dry-sensor technology have enabled easy brain activity data collection thorough a variety of portable brain computer interfaces based on electroencephalography (EEG) technology. This paper proposes a data analysis framework for evaluating the impact of various brainwave frequencies (delta, theta, alpha, beta, and gamma) on human attention. Multiple working scenarios have been created for the subjects targeting to enhance their level of attention. To properly evaluate the data by utilizing the artificial neural networks, several hypotheses have been defined. Their purpose is to group the brainwaves into low to medium frequency type and high-frequency type with the attention and meditation values and attempt to establish interconnections.

Pages: 9 to 14

Copyright: Copyright (c) IARIA, 2022

Publication date: June 26, 2022

Published in: conference

ISSN: 2308-4367

ISBN: 978-1-61208-985-0

Location: Porto, Portugal

Dates: from June 26, 2022 to June 30, 2022