EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Principal Component Analysis <s> A method of de-noising multi-channel EEG signals which combines the principle component analysis (PCA) with density estimation blind source separation (DEBSS) is proposed in this paper. Based on removing high frequency noise in wavelet analysis, the PCA algorithm is used to process the EEG signals to reduce the data dimension. Then, the DEBSS algorithm is adapted to separate the EEG signals which data dimension has been reduced. The main interference is identified and removed by using cross-correlation coefficient and related non-linear parameters to analyze the independent components. Finally, through reconstructing the remaining independent components, the EEG signals without main interference will be obtained. The experimental results show that this method can eliminate the main interference of multi-channel EEG signals rapidly and effectively, meanwhile, it is stable and has strong scalability. <s> BIB001 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Principal Component Analysis <s> In recording the EEG signals are often contamination signal called artifacts. There are different kinds of artifacts such as power line noise, electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). This research will compare two methods for removing artifacts, i.e. ICA and PCA methods. EEG signals are recorded on three conditions, which is normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of the EEG signal is obtained in the range of 12–14 Hz (alpha-beta) either on normal conditions, closed eyes, and blinked eyes. From processing with ICA and PCA methods found that ICA method are better than PCA in terms of the separation of the EEG signals from mixed signals. <s> BIB002
Principal Component Analysis (PCA) converts a set of possibly correlated variables into a set of linearly uncorrelated variables using orthogonal transformation. The linearly uncorrelated variables are called the principal components. The principal components are constructed in such a way that they maximize the variance and the i th principal component is orthogonal to the (i-1) th principal component. The principle behind using PCA as a denoising technique is that the principal components with relatively higher variance compared to the effect of the noise are relatively less noisy. Denoising techniques based on PCA have been presented in BIB001 BIB002 . However, the survey done in  reveals that recent works prefer ICA over PCA since artefacts are better modeled as independent components rather than orthogonal components.