Translated Abstract
ABSTRACT
Electroencephalogram (EEG) is the electrical signal emitted by the brain, and it is the general reflection of the electrophysiological activity of brain neurons on the surface of the cerebral cortex or scalp. Brain signal not only contains a lot of physiological and disease information, but also contains various psychological information of the human body. The extracted EEG signals are easily disturbed by other human biological signals. These interference signals become artifact signals, such as eye signals, Electromyogram signals, Electrocardiogram signals, and electrode noise, which will affect the EEG signals. The existence of artifact signals is also one of the main limitations of EEG acquisition. It is necessary to automatically separate EEG signals from noise and artifacts. This is the premise of EEG analysis for physiological and psychological researches and analysis of EEG. The separation of artifact in EEG has become a hot topic in the field of EEG, and is also the key technology of this thesis.
Based on the domestic and foreign research status of the EEG in the field of artifact removal and the practical needs of applications on different occasions, the method of artifact separation for single channel EEG and the automatic artifacts removal for multi-channel EEG are deeply studied in this thesis. A hardware structure of artifact separation for single channel EEG based on Wavelet-ICA and an automatic removal method of multichannel EEG artifacts based on independent component analysis, hierarchical clustering and wavelet threshold denoising are introduced. For the former, on the basis of comparing the advantages and disadvantages of different methods, the thesis makes an in-depth analysis of the algorithm and gets the best solution. the software simulation is carried out and the optimal scheme is obtained. Then the Verilog HDL is used to design the hardware circuit. Simulation result shows that the designed hardware circuit has good effect on the single channel EEG artifact separation. For the latter, this thesis builds a software system based on independent component analysis, hierarchical clustering and wavelet threshold de-noising. This method can separate the isolated EEG signals and recognize them automatically. In the process of removing artifacts, we use wavelet threshold denoising method to preserve the EEG related signals. And it has been verified that this method has good automatic separation effect for a variety of artifact signals. Finally, the difficulties and shortcomings of the work are summarized, and the future work is prospected.
Translated Keyword
[Artifact, Electroencephalogram, Hierarchical clustering, Independent component analysis, Wavelet threshold denoising, Wavelet transform]
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