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A Brief Review on EEG Signal Pre-processing Techniques for Real-Time Brain-Computer Interface Applications

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posted on 30.09.2021, 05:21 authored by B Venkata PhanikrishnaB Venkata Phanikrishna, Paweł PławiakPaweł Pławiak, Allam Jaya Prakash
Electro Encephalo Gram (EEG) is a monitoring method used in biomedical and computer science to understand brain activity. Therefore, the analysis and classification of these signals play a prominent role in estimating a person’s behavior to certain events. Manually analyzing these signals is very tedious and time-consuming, so an automated scientific tool is required to analyze the brain signals. In this work, the authors are explored various pre-processing segmentation techniques that are helpful in an automatic machine and deep learning-based classification methods available for EEG signal processing. Most of the machine and deep learning methods are followed pre-processing as a common step in classification. Extraction of the basic sub-band components from EEG signals such as delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) is very important in the pre-processing stage. These sub bands of EEG signal have extraordinary evidence related to multiple neurophysiological processes, which are useful for further prediction & diagnosis of diseases and other emotion-based applications. This review paper elaborates various elementary ideas of extracting EEG sub-bands and the role of EEG in Brain-Computer Interface (BCI) in the classification. (Submitted To IEEE reviews in Biomedical Engineering)

History

Email Address of Submitting Author

b.phanikrishna@gmail.com

Submitting Author's Institution

VIT Vellore

Submitting Author's Country

India