A study on EEG signals during eye-closed and eye-open using discrete wavelet transform

2016 ◽  
Author(s):  
Mohd Syakir Fathillah ◽  
Rosmina Jaafar ◽  
Kalaivani Chellappan ◽  
Rabani Remli
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Eeg Signals

scholarly journals EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2505 ◽  
Author(s):  
Fahd A. Alturki ◽  
Khalil AlSharabi ◽  
Akram M. Abdurraqeeb ◽  
Majid Aljalal
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Single Channel ◽  
Autism Spectrum ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals

Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.

Sign in / Sign up

Export Citation Format

Share Document