Ning, Tianhao (2025) Evaluating the Impact of PCA as a Preprocessing Step in EEG Microstate Analysis. Masters thesis, Concordia University.
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Abstract
Evaluating the Impact of PCA as a Preprocessing Step in EEG Microstate Analysis Tianhao Ning, MASc
Concordia University, 2025
Electroencephalography microstate analysis is now an important technique for investigating temporal dynamics of brain activity. Given that data are commonly recorded by a large number of electrodes, the high-dimensional nature of this data introduces considerable computational challenges to applying traditional clustering techniques to microstate analysis.
This study examines the application of Principal Component Analysis (PCA) as a dimensionality reduction technique to address the challenge. The PCA projects high-dimensional data into a lower-dimensional space while retaining the most critical features, which can enhance the performance of clustering algorithms like k-means. Firstly, we reproduced the EEG microstate topographies according to Jia & Zeng's work, "EEG signals respond differently to idea generation, idea evolution and evaluation in a loosely controlled creativity experiment," published in 2021, as a baseline. We then compared these results with those of the PCA-reduced data to test whether the structural characteristics of microstates concerning global explained variance, topography similarities, and coverage are preserved after dimensionality reduction.
We tested the performance of PCA in this way, maintaining 95%, 98%, and 99% variance for the original clean data for different cognitive tasks. Our results showed that PCA reduced processing time and maintained the quality of the data, particularly at 98%. However, the PCA-reduced data results differed from those obtained from the original clean data, indicating that PCA is indeed helpful but does alter the results after all.
It also has broad implications for the interpretation of how dimensionality reduction affects EEG microstate analysis, potentially providing superior techniques for handling high-dimensional neural data. Future studies should investigate the biological significance of PCA's effects on EEG data and the impact of PCA on noise signals and test whether this method is suitable as a preprocessing step for other microstate clustering algorithms to ensure that the conclusions reached analytically are both reliable and valid.
Keywords: Electroencephalography, Principal Component Analysis, Microstate Analysis, Clustering, Dimensionality Reduction
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Ning, Tianhao |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Quality Systems Engineering |
| Date: | July 2025 |
| Thesis Supervisor(s): | Zeng, Yong |
| Keywords: | Electroencephalography, Principal Component Analysis, Microstate Analysis, Clustering, Dimensionality Reduction |
| ID Code: | 995856 |
| Deposited By: | Tianhao Ning |
| Deposited On: | 04 Nov 2025 17:40 |
| Last Modified: | 04 Nov 2025 17:40 |
| Additional Information: | one year embago since we need to submit it to a journal |
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