Signal to noise ratio enhancement in BCI with using ICA method in preprocessing step
Abstract
The quality of Electroencephalography (EEG) signals is low and these signals mostly are being mixed with physiological and non-physiological noises. The Independent Component Analysis (ICA) is an appropriate and common method for reducing the noises and improving the EEG signals quality. This method reduces the physiological and uncommon noises by allocating each signal to its own source. By doing this, signals become independent and noises are being separated from signals. In this paper we use first dataset of forth competition of BCI Competition website, which this dataset has two class and related to motor imagery of hands and feet. We implement two different experiments on this dataset. The difference of these two experiments is in using the ICA method for reducing noise in preprocessing step of brain-computer interface (BCI). Using ICA increased signal to noise ratio and BCI performance. Common Spatial Pattern (CSP) method is used in feature extraction step of both experiments.
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