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Abstract:
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This thesis presented an efficient method of separating EMG artifacts from EEG
signals containing epileptic spikes, specifically helps doctors accurately diagnose
epilepsy by reduced muscular artifacts in EEG.
Firstly, the noisy signals are pre-processed by a combined filter which consists of a
low-pass, a high-pass and a notch filters. After that, we focused on blind source
separation method - SOBI, on the basis that thesis evaluated the quality of this algorithm
with simulated signals and real measured signals from patients. In the next step, the
power spectrum density - PSD method is applied to identify the EMG source and take the
compensation on each channel. We also investigated the effect of the frequency range to
this identification. The algorithm is tested by simulated and experiment signals. The
results were very good signal over noise reduction can represent clearly the epileptic
spikes in the EEG may help clinicians accurately diagnose the patient's situation. |