EEG Noise Cancellation by Stochastic and Deterministic Approaches

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Salsabili, Sina | 2015

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Type of Document: M.Sc. Thesis
Language: English
Document No: 47036 (55)
University: Sharif University of Technology, International Campus, Kish Island
Department: Science and Engineering
Advisor(s): Shamsollahi, Mohammad Bagher
Abstract:
Noise contamination is inevitable in biomedical recordings. In some cases biomedical recordings are highly contaminated with artifacts which make the effective recovering process hard to achieve. Many different methods have been proposed for artifact removal from biomedical signals but introducing an effective method which can present valuable data for medical analysis, is still an ongoing process.
This dissertation focuses on inter-ictal EEG denoising approaches including ICA-based and EMD-based methods and different combination of these methods. These methods are tested on simulated epileptic recordings which are contaminated with real muscle artifact and EEG signal. The denoised signals are evaluated with variety of criteria including signal to noise ratio improvement, root mean square error and source localization. The denoising results of proposed combination methods show outstanding performance comparing to conventional EEG denoising methods
Keywords:
Denoising ; Independent Component Analysis (ICA) ; Empirical Modes Decomposition (EMD)Method ; Interictal Signals ; Muscle Artifact

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