| Summary: | 博士 === 國立中央大學 === 電機工程學系 === 101 === Electroencephalography (EEG)/Magnetoencephlography(MEG) has been applied for investigation of neuroscience and diagnosis of many neurological disorders. The noninvasive EEG recordings are overlapping potentials from spontaneous brain rhythm, physiological artifact and external interference. Accordingly, extraction task-related signal features is crucial in the field of EEG/MEG signal processing. The aim of this study is to develop an EMD-based approach to extract single-trial EEG signal. The EMD method decomposed an EEG/MEG epoch into various scale of sub-band component called intrinsic mode function (IMF) and customized the recognition of task-related component to suppress the task-unrelated component. This dissertation evaluates the performance of EMD in EEG or MEG signal into the applications of steady-state visual evoked potential based brain computer interface (SSVEP-based BCI), sensorimotor mu rhythm extraction and olfactory event-related potential extraction.
To evaluate the performance of SSVEP feature extraction, this study presents an empirical mode decomposition (EMD) and refined generalized zero crossing (rGZC) approach to achieve frequency recognition in SSVEP-based BCI. The EMD-rGZC improves the information transfer rate (ITR).
Event-related desynchronization (ERD) and synchronization (ERS) analysis methods have been widely used in studying movement-related sensorimotor functions in human brain. Movement-related ERD is functionally related to motor planning and initialization, while movement-related ERS is related to motor inhibition and motor cortex resetting. This study developed a single-trial brain rhythm analysis method based on EMD method to discover the mechanisms of ERD and ERS in normal subjects and Parkinson’s patients as well, which could be used as a clinical index for diagnosing Parkinson’s patients.
In the study of olfactory event-related potential (OERP) feature extraction, we developed an EMD-based approach to extract OERP from multi-channel EEG recordings. The EMD approach decomposes a signal into IMFs by iteratively conducting the sifting process. Dual criteria on frequency and spatial template were adopted to facilitate the selection of OERP-related IMFs and to reconstruct single-trial OERP for inter-trial investigation.
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