EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> Summary:A method is presented for investigation of EEG of children with autistic spectrum disorder using complexity and chaos theory with the goal of discovering a nonlinear feature space. Fractal Dimension is proposed for investigation of complexity and dynamical changes in autistic spectrum disord <s> BIB001 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> BackgroundComplex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD.MethodsUsing mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months.ResultsMultiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter.ConclusionsThis proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder. <s> BIB002 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> Abstract This paper presents a methodology for investigation of functional connectivity in patients with autism spectrum disorder (ASD) using Fuzzy Synchronization Likelihood (Fuzzy SL). Fuzzy SLs between and within brain regions are calculated in all EEG sub-bands produced by the wavelet decomposition as well as in the full-band EEG. Then, discriminative Fuzzy SLs between and within different regions and different EEG sub-bands or full-band EEG for distinguishing autistic children from healthy control children are determined based on Analysis of Variation (ANOVA). Finally, the selected features are used as input to an Enhanced Probabilistic Neural Network classifier to make an accurate diagnosis of ASD based on the detected differences in the regional functional connectivity of autistic and healthy EEGs. The methodology is validated using EEG data obtained from 9 autistic and 9 healthy children. The ANOVA test showed high ability of the regional Fuzzy SLs in low frequency bands, delta and theta, as well as alpha band for discriminating the two groups. A high classification accuracy of 95.5% was achieved for distinguishing autistic EEGs from healthy EEGs. It is concluded that the methodology presented in this paper can be used as an effective tool for diagnosis of the autism. Further, the regional Fuzzy SLs discovered in this research can be used as reliable markers in neurofeedback treatments to improve neuronal plasticity and connectivity in autistic patients. <s> BIB003 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> This article shows the efficacy of TWIST, a methodology for the design of training and testing data subsets extracted from given dataset associated with a problem to be solved via ANNs. The methodology we present is embedded in algorithms and actualized in computer software. Our methodology as implemented in software is compared to the current standard methods of random cross validation: 10-Fold CV, random split into two subsets and the more advanced T&T. For each strategy, 13 learning machines, representing different families of the main algorithms, have been trained and tested. All algorithms were implemented using the well-known WEKA software package. On one hand a falsification test with randomly distributed dependent variable has been used to show how T&T and TWIST behaves as the other two strategies: when there is no information available on the datasets they are equivalent. On the other hand, using the real Statlog (Heart) dataset, a strong difference in accuracy is experimentally proved. Our results show that TWIST is superior to current methods. Pairs of subsets with similar probability density functions are generated, without coding noise, according to an optimal strategy that extracts the most useful information for pattern classification. <s> BIB004 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice. <s> BIB005 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> Feature extraction is a process to extract information from the electroencephalogram (EEG) signal to represent the large dataset before performing classification. This paper is intended to study the use of discrete wavelet transform (DWT) in extracting feature from EEG signal obtained by sensory response from autism children. In this study, DWT is used to decompose a filtered EEG signal into its frequency components and the statistical feature of the DWT coefficient are computed in time domain. The features are used to train a multilayer perceptron (MLP) neural network to classify the signals into three classes of autism severity (mild, moderate and severe). The training results in classification accuracy achieved up to 92.3% with MSE of 0.0362. Testing on the trained neural network shows that all samples used for testing is being classified correctlyARPN Journal of Engineering and Applied Sciences <s> BIB006 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> Autism is a devastating disease affecting 12% of newborns. Its incidence is steadily increasing.A growing body of evidence points out that to effectively treat autism, the earliest possible detection is directly proportional to successful therapy. This clearly implies that the availability of an accurate and relatively inexpensive diagnostic method for early diagnosis should be one of the medical community's highest priorities.The relevant involvement of the cerebral cortex in substantially altering cortical circuitry explains the unique pattern of deficits and strengths that characterize cognitive functioning. On the other, this makes the potential usefulness of EEG recording plausible as a biomarker of these abnormalities.Despite this plausibility, very few studies have attempted to use EEG recording in diagnosing autism.Traditional EEG measures apply frequency domain analysis, assume that EEG is stationary and employ linear feature extraction. Novel approaches based on artificial adaptive systems like those employed by us, apply data driven time& frequency domain analysis, assume that EEG is not stationary and use nonlinear feature extraction.To our knowledge, this is the first study that applies an artificial adaptive system to extract interesting features in computerized EEG that distinguishes ASD children from typically developing ones.The results are extremely interesting and open new avenues for confirmatory clinical studies in this important field. Background. Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease.Aim of the study. The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones.Methods. Fifteen definite ASD subjects (13 males; 2 females; age range 714; mean value =10.4) and ten typically developing subjects (4 males; 6 females; age range 712; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers.Results. The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature.Conclusion. This pilot study seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD. <s> BIB007 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements. <s> BIB008 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> ASD Classification Approaches <s> Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a person’s cognition and behaviour. It is a lifelong condition which cannot be cured completely using any intervention to date. However, early diagnosis and follow-up treatments have a major impact on autistic people. Unfortunately, the current diagnostic practices, which are subjective and behaviour dependent, delay the diagnosis at an early age and makes it harder to distinguish autism from other developmental disorders. Several works of literature explore the possible behaviour-independent measures to diagnose ASD. Abnormalities in EEG can be used as reliable biomarkers to diagnose ASD. This work presents a low-cost and straightforward diagnostic approach to classify ASD based on EEG signal processing and learning models. Possibilities to use a minimum number of EEG channels have been explored. Statistical features are extracted from noise filtered EEG data before and after Discrete Wavelet Transform. Relevant features and EEG channels were selected using correlation-based feature selection. Several learning models and feature vectors have been studied and possibilities to use the minimum number of EEG channels have also been explored. Using Random Forest and Correlation-based Feature Selection, an accuracy level of 93% was obtained. <s> BIB009
The results were obtained after running the tests 200 times, except for DNN due to its computationally exhaustivity. Here, the ratio between the training and test set was 80:20. Based on the results of 10x2 crossvalidation, 100% accuracy has shown for the combined dataset using Naïve Bayes and Logistic Regression Classifiers. Using only the eye movement data, Logistic Regression and DNN have achieved 100% accuracy. A data-driven approach is followed by BIB008 , to classify ASD subjects as shown in Fig. 7 . Unlike most of the other studies, EEG data collected from 188 participants were used. It includes 89 Low-Risk Controls (LRC) (among which 3 were diagnosed with ASD) and 99 High Risk for Autism (HRA) (among which 32 were diagnosed with ASD). In addition, the participants were in between the ages of 3 to 36 months of age and were scheduled several visits in that period. During the collection period, bubbles were blown to control the child's behaviour. EEG data from either 64 or 128 channels were recorded but only the channels in the International 10-20 system were used for the analysis. They have extracted features using Sample Entropy, DFA and Recurrence Quantitative Analysis (RQA). For each channel, the 9 features: sample entropy, detrended fluctuation analysis, entropy derived from recurrence plot, max line length, mean line length, recurrence rate, determinism, laminarity and trapping time were generated. The features of interest were filtered using the feature ranking methods (Recursive Feature Selection). .[). .[). BIB009 , 15 (8): 1161 .1183 DOI: 10.3844/jcssp.2019 .1161 .1183 For the classification of ASD or no-ASD, only the data from ASD and LRC subjects were used for training with leave-one-out cross-validation scheme. The HRA subjects (test set) were classified using data from the ASD and LRC subjects as the training set. SVM was used for classification. The distance from the hyperplane which is used as the decision boundary in SVM is used to calculate the severity score between the range of 1-10. Classification using SVM achieved 100% accuracy in distinguishing ASD subjects from the LRC subjects. However, when classifying HRA subjects, classifier's accuracy depreciated as it was challenging for SVM to classify HRA subjects who were placed close to the decision boundary. Another prominent feature of this study is that severity scores were calculated, and they had a strong correlation with the actual severity score. Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm (MS-ROM/I-FAST) is an Artificial Neural Network based system with the capability to extract valuable features from EEG. Mainly it does not require any preliminary preprocessing. The algorithm was able to distinguish Mild Cognitive Impairment and/or Alzheimer's Disease with an accuracy of 94%-98%. The work done in BIB007 , has tried to measure its effectiveness in identifying autistic people. Their work involves 25 participants, 15 ASD (13 males and 2 females; 7-14 years of age; mean -10.4) and 10 typically developing (4 males and 6 females; 7-12 years of age; mean-9.2) individuals. The collected data were resting state EEG obtained while the participants were opening and closing their eyes. Data were collected for 3 minutes at a sampling rate of 256Hz based on the International 10-20 system. The structure of I-FAST is demonstrated in Fig. 8 . It consists of 3 phases: squashing phase, noise elimination phase and classification phase. In normal practice, noise filtering is followed by feature extraction. However, the I-FAST algorithm transforms the EEG channels into feature vectors first using MSE and MS-ROM in the unsupervised squashing phase. Then in the noise elimination phase, irrelevant features are considered as noise and are filtered. The outputs of the MS-ROM are fed into the TWIST algorithm BIB004 to select the best features. Finally, with the help of machine learning algorithms, the classification phase classifies the data. A novel algorithm, MS-ROM, based on the Self Organizing Map (SOM) neural network is presented. It consists of three steps: sampling, projection and ranking. In the sampling phase, EEG signals are sampled many times at different scales and using SOM, the generated subsamples are projected into a two-dimensional grid. In the ranking phase, the generated grids are ranked based on cell frequency. Seven learning algorithms have used for the classification process: sine net neural networks (Sn), logistic regression (LR), sequential minimal optimization (SMO), K-NN, K-Contractive Map (K-CM), Naïve Bayes and Random Forest. This approach was able to produce 100% accuracy consistently with the trainingtesting protocol (11 ASD and 6 control subjects for training and the rest for testing) and with leave-one-out protocol best results were produced by Random Forest with an accuracy of 92.8% and K-Contractive Map and k-Nearest Neighbours with the accuracy of 87.3%. A Computer Aided Diagnosis (CAD) system for ASD diagnosis using DWT, Shannon entropy and Artificial Neural Network (ANN) was proposed in  . EEG data were recorded from 19 subjects, 9 autistic subjects (six males and three females) between 10 and 16 years of age and 10 typically developing males between 9 and 16 years of age. Data were recorded in a relaxing state from 16 channels based on the international 10-20 acquisition system, sampled at 256 Hz and filtered using a band-pass filter. To remove ocular artefacts ICA was applied to the channels located close to the eyes (Fp1, Fp2, F7 and F8). Next, the signals were filtered using an elliptic band-pass filter and segmented into 10 minutes long segments. For better feature extraction, the EEG signal was decomposed into approximation and detail coefficients using DWT. A four-level DWT decomposition with Daubechies-four (db4) wavelet was used and the first four detail coefficients (D1, D2, D3 and D4) and the approximation coefficient (A4) were calculated. Then five statistical features (mean, standard deviation, variance, skewness and kurtosis) and four entropy features (log energy, threshold entropy, Renyi entropy and Shannon entropy) were extracted from all the DWT coefficients and the original EEG signal as demonstrated in Fig. 9 . Two-layer Artificial Neural Network (ANN) was used for classification. Using 10-fold cross-validation, accuracy, sensitivity and specificity were measured. The classification was carried out in several stages. In stage one, statistical features and entropy features were used separately as inputs to ANN keeping the segment length fixed. After identifying standard deviation and Shannon entropy as the best features, further optimizations were carried out in the next stages. Tests were carried out to find the optimum segment length and frequency band (wavelet coefficient). Results obtained using overlapping and non-overlapping segments were also analysed. Best segment length was found to be 50 sec. Similarly, detail coefficients D1, D2, D3 and D4 produced the best accuracy of 98.9%. The test results for overlapping and non-overlapping segments revealed that 60 sec long segments with half-segment overlapping produce the best accuracy of 99.7%. The results conclude that the best approach for the CAD system is to extract standard deviation and Shannon entropy from the detail coefficients using 60 sec long half overlapping segments. BIB006 , have proposed a classification technique based on DWT. The EEG dataset, used in this research was recorded during stimulation of three tastes (salty, sour and sweet). Data were recorded from 30 ASD subjects between 3 and 10 years of age based on the International 10-20 system at a sampling rate of 500Hz. They were identified with 3 levels of autism, 5 subjects with mild autism, 11 subjects with moderate autism and 14 subjects with severe autism. Only the channels related to the taste sensory (C 3 , C 4 and C z ) were selected for analysis. Fig. 10 shows the process. Noise filtering was performed using voltage threshold method and bandpass filter with band frequency 0.4Hz to 60Hz was applied. In the feature extraction phase, DWT was applied using db4 as the mother wavelet. Standard deviations of the alpha frequency band (8Hz -16Hz) of the three channels for three different tastes were calculated and used as inputs to the classification phase. A two-layer ANN was used for classification. Through trial and error data division of 65% for training, 10% for testing and 15% for validation was found to be producing the best results of accuracy 92.3% with a mean squared error of 0.0362. One significant feature of this methodology is the usage of a validation set. Other related studies only used training and test sets. When we adjust the model continuously based on the results obtained by evaluating the model on the test set, most likely we would end up overfitting the model to the test set. By using a validation set, the model can be evaluated for overfitting to the test set. The authors of BIB005 analyzed the functional connectivity of the brain using phase synchronization to find a reliable biomarker for diagnosing ASD. Studies suggest that inactivation of brain circuitry associated with face processing might be the cause for the challenges faced by autistic children to understand facial expressions. Hence, the connectivity of the brain was explored in order to find differences between ASD and normal children during face perception. Data were collected from 24 subjects, 12 children with ASD between 6 and 13 years of age (average = 10.2) and 12 typically developing children between 6 and 13 years of age (average = 9.7) while performing face perception tasks. Data were obtained from 128 channels at a sampling rate of 250 Hz and filtered within the range of 0.5 Hz to 50 Hz using a band-pass filter. Fig. 11 shows the methodology proposed in the study. Continuous Wavelet Transform (CWT) was applied and phase synchronized states (synchrostates) were obtained. Since obtaining synchrostates is a long procedure, we have omitted the details. The brain connectivity graph was built where the EEG electrodes are the nodes and the synchronization values between them are the weights of the edges. Modularity, transitivity, characteristic path length, global efficiency, radius and diameter of the brain connectivity graph were selected as features for the classification task. These six features were calculated corresponding to the three facial stimuli (fear, happy and neutral) with minimum and maximum occurring states. Thus 36 features were obtained in total. Fisher's discriminant ratio was used for feature ranking. Nine different subsets of the features were created and used for classification separately. Discriminant analysis and SVM with polynomial kernel were used for classification. When using all the min and max state features for all three stimuli and all the max features for all three stimuli, classification using SVM with secondorder kernel produced the best accuracy of 94.7% with sensitivity 85.7% and specificity 100%. BIB003 , have proposed an approach which uses Fuzzy Synchronization Likelihood (Fuzzy SL). This approach analyses the functional connectivity of the brain of normal and autistic children using Fuzzy SL and diagnoses ASD based on that. An abstract workflow of their approach is demonstrated in Fig 12. EEG data were collected from 18 subjects, 9 autistic children between 7 and 13 years of age (average = 10.8) and 9 typically developing children between 7 and 13 years of age (average = 11.1), according to International 10-20 system at a sampling rate of 256 Hz. Applying Butterworth filter EEG is filtered within the range of 1-60Hz and using the wavelet transform signal was divided into 5 frequency bands: gamma, beta, alpha, theta and delta. The electrode locations were categorized into 7 regions: prefrontal, frontal, right temporal, left subjects for testing) 100 times and obtaining the average, an accuracy of 95.5% was obtained with a variance of 1.2%. Since the number of subjects involved in the study is low, using cross-validation would have increased the reliability of the results and allowed more data to be used for training. In addition to the classification, the authors also claim that measured regional Fuzzy SLs can be used in the neurofeedback treatment as well. Another study by the same authors to diagnose ASD using improved visibility graph (VG) fractality is presented in  . Power of scalefreeness of VG (PSVG) and improved PSVG were evaluated in their study for effectiveness in classifying ASD. Visibility graphs convert a fractal time series to a scale-free graph characterized by P(k) = k -r , where P is the probability distribution of the edges, k is the order of the nodes and r is the power of scale-freeness. A scalefree graph is a graph whose degree distribution follows a power law. PSVG is the value of the slope when log 2 [P(k)] is plotted against log 2 [k]. The same data used in their previous study BIB001 was used for this study and the same methodology as in the previous study was followed until wavelet decomposition. The details of their previous study will be discussed later in this section. The classification methodology is presented in Fig. 13 . PSVG and improved PSVG values were calculated for all 5 sub bands. Using ANOVA features with pvalues less than 0.01 were selected as inputs to the EPNN. PSVG computed for the beta band and improved PSVG computed for beta and alpha bands were selected. About 80% of the data were selected for training and 20% were used for testing. The classification was performed 200 times. Classification based on improved PSVG achieved an average accuracy of 95.5% with 1.7% variance, while classification based on PSVG achieved an average accuracy of 84.2% with 1.8% variance. The diagnosis approach proposed in BIB002 ) is one of the initial attempts which utilized analysis of EEG data to produce a biomarker for children at high risk for ASD. The goal of their study was to demonstrate that mMSE (modified multiscale entropy) can be used as a biomarker to distinguish typically developing children from children at high risk for ASD. The children with an older sibling diagnosed with ASD were categorized as high risk for ASD. The workflow of the approach is presented in Fig. 14. Their study included 79 participants, among which 46 were at high risk for ASD and 33 controls. Similar to the other studies, the control subjects were defined on the basis that they have a typically developing older sibling and no family history of neurodevelopmental disorders. The participants were between 6 to 24 months of age. From some participants, data were collected multiple times at different ages. Those data were considered as independent datasets, hence even though there were only 79 participants, a total of 143 sets of data were included in the study. EEG data were collected using a 64-channel Sensor Net System while blowing bubbles. Signals were band-pass filtered at 0.1 to 100.0 Hz and sampled at a rate of 250Hz. Out of the 2 minutes long recordings, only 20 seconds long continuous segments were used for the analysis. As the first step for calculating the mMSE values, coarse-grained series from scales 1 to 20 were computed for each channel. Then the entropy values were calculated using modified sample entropy (mSE). The entropy values calculated using mSE are more robust to noise and consistent with short time series. Finally, for each coarse-grained series from scales 1 to 20, mMSE is defined as a series of mSE values. SVM, K-NN and Naïve Bayes algorithms were used for classification. The models were evaluated using 10-fold cross-validation. Unlike the other studies, boys and girls have been classified separately and as a unified complete set as well. Moreover, classification was performed separately for different age groups, at 6, 9, 12, 18 and 24 months of age. For the dataset combining both boys and girls, K-NN achieved the maximum accuracy of 90% for 9 and 18 months age groups. For the boys, SVM produced 100% accuracy for the 9 months age group and for the girls, SVM produced the maximum accuracy of 80% for the 6 months age group. BIB001 , have proposed a methodology based on fractality and a wavelet-chaosneural network for diagnosis of ASD as illustrated in Fig. 15 . They introduced the idea of using Fractal Dimensions (FDs) as features. FD is a non-integer dimension which shows the degree of complexity and self-similarity of a signal. Eye-closed EEG data were collected from 17 subjects, 9 ASD children (6 to 13 years old) and 8 typically developing children (7 to 13 years old). International 10-20 standard was used for electrode placement and data were recorded from 19 channels at a sampling rate of 256Hz. This dataset was used by the authors in  as well. Applying bandpass filters, signals were filtered within 0-60Hz and using wavelet decomposition gamma, beta, alpha, theta and delta bands were obtained. After preprocessing the signal, Higuchi's Fractal Dimension (HFD) and Katz's Fractal Dimension (KFD) algorithms were used for FD computation of the EEG signals. Statistically significant FDs with a p-value less than 0.01 were selected using ANOVA. Three features were obtained and were used for classification using a twolayer Radial Basis Function Neural Network (RBFNN). 82% of the data were used for training and 18% were used for testing. The classification was performed 100 times using random subsampling. RBFNN produced results with 90% average accuracy and 0.15% variance. Non-complex implementation, using only the holdout method. Classify ASD at 3 severity levels, Use a taste-based EEG data BIB005 Classification The considered ASD diagnostic approaches were selected based on the recent studies, that have applied machine learning approaches for ASD classification, between the year 2010 and 2018. We have explored the details of EEG datasets, techniques used, the methodology followed, significant aspects and the results of each of the related study. We have compared the pre-processing, feature extraction and classification techniques used by each of the studies in identifying ASD subjects. Thus, researchers and practitioners can use this survey to understand the useful and effective techniques.