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