The Study On Bioelectrical Signals Based On Hilbert-Huang Transform And Support Vector Machine

Research on the bioelectrical signal, such as surface electromyographic signal (sEMG) and electroencephalography (EEG), are very useful and meaningful. For example, sEMG can reflect the change of muscular fatigue at certain extent, and one type of brain-computer interface (BCI), which also be referred as EEG-based brain computer interface (BCI), users self-modulate brain activity as detected by EEG.The new method Hilbert-Huang Transform (HHT) has the powerful ability of analyzing nonlinear and non-stationary data in both time and frequency aspect together. The method have self-adaptive basis and is better for feature extraction as we can obtain the local and instantaneous frequency of the signals.In this paper,we chose an experiment of the static biceps data of twelve adult subjects under the maximal voluntary contraction (MVC) of 80%.The sEMG experimental results proved that this method, as a new thinking has an obvious potential for the biomedical signal analysis. We then use the HHT method combine with the Support Vector Machine (SVM) classifier in a binary classification of an EEG of visual feedback dataset. During the course of the EEG signals feature extraction, we introduced the method which combined the slow cortical potentials (SCPs) and the specific energy from the time-frequency domain in high /?-band via the Hilbert-Huang Transform (HHT), The Support Vector Machines (SVM), which have been widely used in pattern recognition and regression, were utilized for data classification. We obtained good accuracy from our experimental results, using the HHT and SVM methods on the combined information of the EEG signals.