EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> EEG as a Diagnostic Test <s> Alzheimer's disease (AD) is the most common neurodegenerative disorder characterized by cognitive and intellectual deficits and behavior disturbance. The electroencephalogram (EEG) has been used as a tool for diagnosing AD for several decades. The hallmark of EEG abnormalities in AD patients is a shift of the power spectrum to lower frequencies and a decrease in coherence of fast rhythms. These abnormalities are thought to be associated with functional disconnections among cortical areas resulting from death of cortical neurons, axonal pathology, cholinergic deficits, etc. This article reviews main findings of EEG abnormalities in AD patients obtained from conventional spectral analysis and nonlinear dynamical methods. In particular, nonlinear alterations in the EEG of AD patients, i.e. a decreased complexity of EEG patterns and reduced information transmission among cortical areas, and their clinical implications are discussed. For future studies, improvement of the accuracy of differential diagnosis and early detection of AD based on multimodal approaches, longitudinal studies on nonlinear dynamics of the EEG, drug effects on the EEG dynamics, and linear and nonlinear functional connectivity among cortical regions in AD are proposed to be investigated. EEG abnormalities of AD patients are characterized by slowed mean frequency, less complex activity, and reduced coherences among cortical regions. These abnormalities suggest that the EEG has utility as a valuable tool for differential and early diagnosis of AD. <s> BIB001 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> EEG as a Diagnostic Test <s> The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t- f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented. <s> BIB002 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> EEG as a Diagnostic Test <s> Autism spectrum disorders (ASDs) are pervasive neurodevelopmental conditions characterized by impairments in reciprocal social interactions, communication skills, and stereotyped behavior. Since EEG recording and analysis is one of the fundamental tools in diagnosing and identifying disorders in neurophysiology, researchers strive to use the EEG signals for diagnosing individuals with ASD. We found that studies on ASD diagnosis using EEG techniques could be divided into two groups in which where analysis was based on either comparison techniques or pattern recognition techniques. In this paper, we try to explain these two sets of algorithms along with their applied methods and results. Lastly, evaluation measures of diagnosis algorithms are discussed. <s> BIB003 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> EEG as a Diagnostic Test <s> Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental disorder. Individuals with ASD often have deficits in social communication skills as well as adaptive behavior skills related to daily activities. We have recently designed a novel virtual reality (VR) based driving simulator for driving skill training for individuals with ASD. In this paper, we explored the feasibility of detecting engagement level, emotional states, and mental workload during VR-based driving using EEG as a first step towards a potential EEG-based Brain Computer Interface (BCI) for assisting autism intervention. We used spectral features of EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings of behavioral engagement, enjoyment, frustration, boredom, and difficulty to train a group of classification models. Seven classification methods were applied and compared including Bayes network, naive Bayes, Support Vector Machine (SVM), multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states. Such results may lead to an adaptive closed-loop VR-based skill training system for use in autism intervention. <s> BIB004
A behaviour-independent approach can be designed based on Electroencephalography (EEG). EEG records the electrical activity of the brain by recording the electrical impulses of different frequencies used by neurons for communications through electrodes attached to the scalp. EEG is being studied for a long time to support medical diagnosis  . The abnormalities in EEG signals have been found to be reliable biomarkers for medical conditions such as epileptic seizures BIB002 ) and Alzheimer's disease BIB001 . In addition to diagnosis, novel approaches to facilitate treatment plans using EEG have also been proposed BIB004 . Literature reveals that two different types of EEG based approaches were proposed in the past to diagnose ASD: (1) comparison method and (2) pattern recognition and classification approach BIB003 . In the first approach, EEG signal characteristics of typically developing individuals are compared with that of individuals with ASD. This paper focuses on the second approach which adopts machine learning algorithms to analyse the EEG signal and classify ASD.