Earticle

초록

영어

Electroencephalography (EEG) is a vital part of neuroscience research, providing insight into brain activity by noninvasive measurement of electrical potentials. The raw EEG data is frequently contaminated with artifacts from many diversified sources, such as eye movements, muscle movements and environmental noise. Efficient preprocessing is vital for an improved efficacy of a signal, along with its robustness. This paper presents a structured procedure for preprocessing an EEG signal for extraction of event-related brain potentials (ERPs) and for decomposing the signal into its frequency bands. The BrainVision system recorded the EEG data, and before analysis, we carried out preprocessing steps, which are: set channel types, use standard montages, resampling, referencing, filtering, Independent Component Analysis (ICA), event handling, and segmenting the frequency bands. We applied band-pass filters to isolate delta, theta, alpha, and beta bands, followed by Short-Time Fourier Transform (STFT), which provides spectrogram for each frequency band, and finally, our purpose was to compute ERP (Event-Related Potential) time-locked averages over defined channels using the processed data. The outcomes of the above procedure demonstrate the efficiency of our process in removing artifacts and enhancing the clarity of our signal by showing the distinct patterns in the different bands of interest as well as the ERP waveforms. This methodological approach underscores the importance of meticulous preprocessing in EEG analysis, paving the way for more accurate and interpretable results in cognitive and clinical research. By following the methodology in this paper, researchers can have more accurate and interpretable results, either for cognitive or clinical research. Incorporating advanced signal processing technology, this study proposes a powerful framework for the EEG data analysis, which will provide a thorough understanding and exploration of the dynamics of the brain.

목차

Abstract
1. Introduction
2. Materials and Methods
2.1. Data Acquisition
2.2. Resampling and referencing
2.3. Filtering
2.4. ICA(Independent Component Analysis)
2.5. Baseline correction and rejection of bad epoch
3. Results and Discussion
3.1. ERPs (Event-Related Potentials)
4. Conclusion
References

키워드

저자

• Sarreha T. Rikta [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ]
• Jörg Stadler [ Combinatorial Neuroimaging Core Facility, Leibniz Institute for Neurobiology, 39118 Magbeburg, Germany ]
• André Brechmann [ Combinatorial Neuroimaging Core Facility, Leibniz Institute for Neurobiology, 39118 Magbeburg, Germany ]
• Wonsang You [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  선문효정학술연구회 학술대회 프로시딩

• 간기

  반년간

• 수록기간

  2023~2026

• 십진분류

  KDC 238 DDC 289