Measurement and Analysis of Electroencephalogram (EEG) Signals Using Pre-Amplification and Filtering Techniques

The primary goal of this study is to provide students with practical experience in measuring and analyzing electroencephalogram (EEG) signals. This involves understanding the effects of visual stimulation on alpha wave activity and managing interference signals to ensure accurate recordings. The human brain’s cortex contains numerous neurons that can synchronize, resulting in rhythmic behaviors detectable as electrical signals on the scalp. These signals, known as EEGs, are crucial for studying brain activity and diagnosing neurological conditions. Different states of alertness and external stimuli affect these signals, which can be categorized into delta, theta, alpha, and beta waves based on their frequency.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/siitme63973.2024.10814851
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

Measurement and Analysis of Electroencephalogram (EEG) Signals Using Pre-Amplification and Filtering Techniques

Emilian Ceuca, Pena Mădălina, Gomboş Andreea
EEG and Brain-Computer Interfaces
article

Measurement and Analysis of Electroencephalogram (EEG) Signals Using Pre-Amplification and Filtering Techniques

Emilian Ceuca, Pena Mădălina, Gomboş Andreea
article en

Abstract

The primary goal of this study is to provide students with practical experience in measuring and analyzing electroencephalogram (EEG) signals. This involves understanding the effects of visual stimulation on alpha wave activity and managing interference signals to ensure accurate recordings. The human brain’s cortex contains numerous neurons that can synchronize, resulting in rhythmic behaviors detectable as electrical signals on the scalp. These signals, known as EEGs, are crucial for studying brain activity and diagnosing neurological conditions. Different states of alertness and external stimuli affect these signals, which can be categorized into delta, theta, alpha, and beta waves based on their frequency.

Technical University of Cluj-Napoca (RO), 1 Decembrie 1918 University (RO)
Openalex Percentile: Top 74%
EEG and Brain-Computer Interfaces
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