Open AccessJournal Article
Feature extraction of EEG signal using wavelet transform for autism classification
TLDR
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 and used to train a multilayer perceptron (MLP) neural network to classify the signals into three classes of autism severity.Abstract:
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 Sciencesread more
Citations
More filters
Journal ArticleDOI
Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition
Enas Abdulhay,Maha Alafeef,Maha Alafeef,Loai Alzghoul,Miral Al Momani,Rabah M. Al abdi,N. Arunkumar,Roberto Munoz,Victor Hugo C. de Albuquerque +8 more
TL;DR: An EEG-based quantitative approach intended for automatic discrimination between children with typical neurodevelopment and children with ASD is presented, which indicates significant differences between SODP area values of children with neurotypical development and those diagnosed with ASD.
Proceedings ArticleDOI
Fully Data-driven Convolutional Filters with Deep Learning Models for Epileptic Spike Detection
TL;DR: A fully data-driven method that automatically determines EEG frequency bands of interest is introduced and results of the cross-subject validation indicate that a classical support vector machine with fixed preprocessing achieves comparable performance in the classification with fullyData-driven models.
Journal ArticleDOI
EEG-based single-channel authentication systems with optimum electrode placement for different mental activities
Mahsa Zeynali,Hadi Seyedarabi +1 more
TL;DR: Channel optimization can obtain higher performance by reducing the number of EEG channels and defined the optimum electrode placement for different mental activities by Neural Network classifier.
Journal ArticleDOI
EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review
Gunavaran Brihadiswaran,Dilantha Haputhanthri,Sahan Gunathilaka,Dulani Meedeniya,Sampath Jayarathna +4 more
TL;DR: A survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning is presented, exploring different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model.
Journal ArticleDOI
Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.
TL;DR: Children with sever and mild ASD had different IMFs features, SODP plotting, elliptical area and CTM values, which indicates their greater EEG variabilities and their greater inability to suppress their improper behavior.
References
More filters
Journal ArticleDOI
The brainweb: phase synchronization and large-scale integration.
TL;DR: It is argued that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.
Fundamentals of eeg measurement
TL;DR: This review article presents an introduction into EEG measurement, a completely non-invasive procedure that can be applied repeatedly to patients, normal adults, and children with virtually no risk or limitation.
Journal ArticleDOI
Classification of human emotion from EEG using discrete wavelet transform
TL;DR: The average classification rate and subsets of emotions classification rate of two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA), are presented for justifying the performance of the emotion recognition system.
Journal ArticleDOI
Classification of EEG signals using the wavelet transform
TL;DR: An artificial neural network technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals, which provides a potentially powerful technique for preprocessing EEG signals prior to classification.
Proceedings ArticleDOI
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
TL;DR: The application of neural network models for classification of electroencephalogram (EEG) signals was described and it was confirmed that the proposed scheme has potential in classifying the EEG signals.
Related Papers (5)
Classifying discrete interval densities of EEG signals by using DWT and SVM
Umut Orhan,Emre Gurbuz +1 more