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Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques
Ahmad Chaddad1,2, Yihang Wu1, Reem Kateb3
1School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|July 29, 2023
Summary
This review surveys electroencephalography (EEG) signal processing, covering acquisition, denoising, feature extraction, and classification. It highlights current limitations and future trends in analyzing complex EEG data for applications like brain-computer interfaces.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Signal Processing
Background:
- Electroencephalography (EEG) signals are complex and noninvasive, with wide applications in sleep studies and brain-computer interfaces.
- Advanced preprocessing and feature extraction methods are crucial for analyzing intricate EEG data.
Purpose of the Study:
- To conduct a comprehensive review of electroencephalography (EEG) signal processing techniques.
- To summarize findings from major scientific and engineering databases on EEG signal analysis.
Main Methods:
- Systematic literature search across major scientific and engineering databases.
- Comprehensive review encompassing EEG acquisition, pretreatment (denoising), feature extraction, classification, and applications.
- Detailed discussion and comparison of various EEG signal processing methods.
Main Results:
- Identified and summarized diverse methods for EEG signal processing from acquisition to application.
- Presented a detailed comparison of existing techniques used in EEG analysis.
- Highlighted current limitations in EEG signal processing methodologies.
Conclusions:
- Future development trends in EEG signal processing techniques were analyzed.
- Suggestions for future research directions in the field of EEG signal processing were provided.
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