Contents
What is ICA used for in EEG?
Independent Component Analysis (ICA) is often used at the signal preprocessing stage in EEG analysis for its ability to filter out artifacts from the signal. The benefits of using ICA are the most apparent when multi-channel signal is recorded.
What is a component in EEG?
ERP components are defined by their polarity (positive or negative going voltage), timing, scalp distribution, and sensitivity to task manipulations. The bottom of Figure 1 illustrates how the small ERP components are embedded in the background EEG which is at least an order of magnitude larger.
How are ICA and LDA used to classify EEG signals?
EEG signals were decomposed into frequency sub-bands using DWT. Then a set of statistical features was extracted from these sub-bands to represent the distribution of wavelet coefficients. PCA, ICA and LDA are used to reduce the dimension of data.
How is a feature extracted from an EEG?
The ICA finds the unmixing matrix (W) and then projects the whitened data onto that matrix for extracting independent signals. [8] The feature extraction methods of EEG are as follows: Then, S = BX = W-1/2X with Cov (S) = I and W are orthonormal. [9]
How are signal processing and analysis frameworks used in EEG?
In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients.
How is DWT used to classify EEG signals?
In this work, DWT has been applied for the time–frequency analysis of EEG signals for the classification using wavelet coefficients. EEG signals were decomposed into frequency sub-bands using DWT. Then a set of statistical features was extracted from these sub-bands to represent the distribution of wavelet coefficients.