Journal ArticleDOI
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.
Hafeez Ullah Amin,Aamir Saeed Malik,Rana Fayyaz Ahmad,Nasreen Badruddin,Nidal Kamel,Muhammad Hussain,Weng-Tink Chooi +6 more
TLDR
It is demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.Abstract:
This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.read more
Citations
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Journal ArticleDOI
Major depressive disorder
Christian Otte,Stefan M. Gold,Stefan M. Gold,Brenda W.J.H. Penninx,Carmine M. Pariante,Amit Etkin,Maurizio Fava,David C. Mohr,Alan F. Schatzberg +8 more
TL;DR: An overview of the current evidence of major depressive disorder, including its epidemiology, aetiology, pathophysiology, diagnosis and treatment, is provided.
Journal ArticleDOI
A review of epileptic seizure detection using machine learning classifiers.
TL;DR: An overview of the wide varieties of techniques based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black- box’ will give a detailed understanding about seizure detection and classification, and research directions in the future.
Journal ArticleDOI
Analysis of EEG signals and its application to neuromarketing
TL;DR: A predictive modeling framework to understand consumer choice towards E-commerce products in terms of “likes’ and “dislikes” by analyzing EEG signals is proposed and the framework can be used for better business model.
Journal ArticleDOI
Classification of EEG Signals Based on Pattern Recognition Approach.
Hafeez Ullah Amin,Wajid Mumtaz,Ahmad Rauf Subhani,Mohamad Naufal Mohamad Saad,Aamir Saeed Malik +4 more
TL;DR: The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction and suggests the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.
References
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Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Journal ArticleDOI
Physiological time-series analysis using approximate entropy and sample entropy
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Journal ArticleDOI
Machine learning classifiers and fMRI: a tutorial overview
TL;DR: This tutorial overview shows how, in addition to answering the question of 'is there information about a variable of interest' ( pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' and 'how is that information encoded' (pattern characterization).
Journal ArticleDOI
The Raven's progressive matrices: change and stability over culture and time.
TL;DR: Stability and variation in the norms for the Raven's Progressive Matrices Test for different cultural, ethnic, and socioeconomic groups on a worldwide and within-country basis are summarized and a possible explanation for the variation in norms over time is offered.