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Strategies for Effective EEG Preprocessing
Alamoudi, Nora Mustafa, Lama Bagis, Fatima
Alamoudi, Nora
Mustafa, Lama
Bagis, Fatima
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Date
2025-04-01
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Abstract
The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology; however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produ
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Effat University