EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> EEG Pre-processing Techniques Independent Component Analysis <s> We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation. <s> BIB001 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> EEG Pre-processing Techniques Independent Component Analysis <s> This paper presents a review on signal analysis method for feature extraction of electroencephalogram (EEG) signal. It is an important aspect in signal processing as the result obtained will be used for signal classification. A good technique for feature extraction is necessary in order to achieve robust classification of signal. Considering several techniques have been implemented for extracting features in EEG signal, we only highlight the most commonly used for schizophrenia. The techniques are Hilbert-Huang transform, Principal Component Analysis, Independent Component Analysis and Local Discriminant Bases. Despite of their drawbacks, they can be applied which depends on the aim of a research, parameters and the data collected. Nevertheless, these techniques can be modified so that the new algorithm can overcome the shortcomings of original algorithm or algorithm beforehand. The modified Local Discriminant Bases algorithm is introduced in the present paper as another powerful adaptive feature extraction technique for EEG signal which is not reported elsewhere in investigating schizophrenia. <s> BIB002 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> EEG Pre-processing Techniques Independent Component Analysis <s> Autism spectrum is one of the serious disorders influencing mental capacities and hence behavior. The present work investigates the potential of detecting irregularities of EEG activity and connectivity in Autism Spectrum by studying the inter-channel stability of EEG components point-by-point pulsation as well as by frequency 3D mapping. First, Empirical Mode Decomposition is used to find the Intrinsic Mode Functions of every EEG channel. Second, the Phase derivatives of analytic intrinsic modes are then calculated. Third, each phase derivative function obtained for an intrinsic mode is plotted versus the phase derivative function of the counterpart intrinsic mode in another channel. Finally, the stability loops found out are then analyzed. Furthermore, 3D mapping of EEG frequency bands (Delta, Theta, Alpha and Beta) is conducted and examined. <s> BIB003
Independent Component Analysis (ICA) is a multivariate analysis which decomposes the original signal into a set of Independent Components (ICs). It separates the signals from different sources from a set of mixed signals. Two important assumptions are made in ICA: (1) the signals from different sources are independent of each other and (2) independent components have non-gaussian distribution. Artefact removal in EEG signals using ICA is a three-step process: (1) decomposing into ICs, (2) discarding standalone ICs and (3) concatenating the remaining ICs to form an artefact-free signal  . Popular EEG signal processing tools including EEGLAB provide functionalities to perform ICA BIB001 . Even though multiple ICA algorithms exist, FastICA, Infomax and JADE are being widely used BIB002 . Several studies report second-order blind identification (SOBI), an ICA algorithm, as a successful technique to remove all types of artefacts from the EEG signal  . ICA has been used as a pre-processing technique for ASD classification in  . It has also been used in BIB003 as a pre-processing step to detect abnormal EEG activities and neural connectivity in autistic individuals.