Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing
Giulia Cisotto1,2, Davide Chicco1,3
1Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Milan, Italy.
Peerj. Computer Science
|September 24, 2024
Summary
This study offers ten essential tips for beginners to avoid common errors in electroencephalography (EEG) signal processing. Following these guidelines ensures more reliable and robust results in brain research.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Computational Science
Background:
- Electroencephalography (EEG) records human brain's electrical activity.
- Advancements in technology have increased EEG data availability and processing ease.
- However, increased accessibility also raises the risk of analytical errors.
Purpose of the Study:
- To provide practical guidelines for accurate EEG signal processing.
- To help researchers, especially beginners, avoid common pitfalls in data analysis.
- To improve the reliability and robustness of findings in clinical neuroscientific research.
Main Methods:
- Development of ten concise tips for EEG data analysis.
- Focus on common mistakes and how to prevent them.
- Guidelines cover what to do, how to do it, and what to avoid.
Main Results:
- A list of actionable recommendations for EEG signal processing.
- Emphasis on best practices to ensure data integrity.
- The tips aim to enhance the quality of scientific outcomes.
Conclusions:
- Adherence to the provided tips can significantly improve EEG analysis quality.
- This can lead to more dependable results in brain research.
- The guidelines support the advancement of clinical neuroscientific knowledge by minimizing errors.
