Autism spectrum disorder (ASD) is a name for a group of neurodevelopmental conditions that are characterized by some degree of impairment in social interaction, verbal and non-verbal communication, and difficulty in symbolic capacity and repetitive behaviors. The only protocol followed currently for ASD diagnosis is the qualitative behavioral assessment by experts through internationally established descriptive scaling standards. The assessment can, therefore, be affected by the degree of the evaluator experience as well as by the level of the descriptive standard robustness. This paper presents an EEG-based quantitative approach intended for automatic discrimination between children with typical neurodevelopment and children with ASD. The suggested work relies on second-order difference plot (SODP) area as a discriminative feature: First, every EEG channel in a 64 electrode cap—for every volunteer—is decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD). Next, the second-order difference plot for the first ten intrinsic mode functions—of every channel—is sketched. Third, the value of the elliptical area —for every plot—is calculated. The 95% confidence ellipse area is used as the discriminative feature. Fourth, paired t-student test is applied to the vectors consisting of discriminative feature values for counterpart channels/IMFs (e.g., channel FPz/IMF7 in autistic and neurotypical) for all volunteers. Finally, principal component analysis (PCA) and neural network (NN) are applied to the SODP area feature matrix for two-class classification (ASD and neurotypical). Moreover, the 3D mapping of EEG SODP area values was implemented and analyzed. The obtained results show that the conducted t-student tests yield values of less than 0.05, and that the NN two-class classification based on SODP features leads to a 94.4% accuracy, which indicates significant differences between SODP area values of children with neurotypical development and those diagnosed with ASD. The obtained results have also been emphasized by the analysis of the findings of the performed 3D mapping.

Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition

Epilepsy is a chronic disorder that causes unprovoked, recurrent-seizures. Characteristic spikes are often observed in the electroencephalogram (EEG) of epileptic patients in order to diagnose the disorder. Several methods have been investigated to automatically detect such spikes. The most common methods employ sub-band decomposition with discrete wavelet transform (DWT) or other filters to preprocess the EEG data before feeding it into a machine learning model. This paper introduces a fully data-driven method that automatically determines EEG frequency bands of interest. The raw signal is fed into a convolutional layer to detect suitable frequency bands, followed by a feedforward convolutional neural network (CNN) model or recurrent neural network (RNN) models for epileptic spike and non-spike classification. Fitting data of six patients, annotated by an epilepsy specialist, resulted in a convolutional layer with a frequency characteristic similar to bandpass filters. This result strongly justifies limiting the bandwidth of a signal, as done in previous studies. Moreover, results of the cross-subject validation indicate that a classical support vector machine with fixed preprocessing achieves comparable performance in the classification with fully data-driven models.

Fully Data-driven Convolutional Filters with Deep Learning Models for Epileptic Spike Detection

Background Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is an addition to biometric authentication systems, which has many advantages over others. In this paper, we study the performance of a single channel brainwave-based authentication systems and select optimum channels based on mental activities. Methods In this study, we used a dataset with five mental activities with seven subjects (325 samples). The EEG based authentication system includes three pre-processing steps, feature extraction, and classification. Features for Subject Authentication, are obtained from discrete Fourier transform, discrete wavelet transform, autoregressive modeling, and entropy features. Then these features are classified using the Neural Network, Bayesian network and Support Vector Machine. Results We achieved accuracy in the range of 97–98% mean accuracy with Neural Network classifier for single-channel authentication system with optimum electrode placement for mental activity. We also analyzed the authentication system independently from the type of mental activity and chose channel O2 as the optimum channel with an accuracy of 95%. Conclusions Channel optimization can obtain higher performance by reducing the number of EEG channels and defined the optimum electrode placement for different mental activities.

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities

Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions.

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EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

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

Chuin Cheong Lung

Papers

Feature extraction of EEG signal using wavelet transform for autism classification