Classification of EEG signals based on mean-square error optimal time-frequency features

2018 ◽  
Author(s):  
Rachele Anderson ◽  
Maria Sandsten
Keyword(s):  
Mean Square Error ◽  
Optimal Time ◽  
Mean Square ◽  
Eeg Signals ◽  
Time Frequency ◽  
Frequency Features

scholarly journals Classification of EEG Signals Using Adaptive Time-Frequency Distributions

2016 ◽  
Vol 23 (2) ◽  
pp. 251-260 ◽  
Author(s):  
Nabeel A. Khan ◽  
Sadiq Ali
Keyword(s):  
Eeg Signals ◽  
Concentrate Energy ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Adaptive Kernel ◽  
Adaptive Time ◽  
Frequency Features ◽  
Total Accuracy

Abstract Time-Frequency (t-f) distributions are frequently employed for analysis of new-born EEG signals because of their non-stationary characteristics. Most of the existing time-frequency distributions fail to concentrate energy for a multicomponent signal having multiple directions of energy distribution in the t-f domain. In order to analyse such signals, we propose an Adaptive Directional Time-Frequency Distribution (ADTFD). The ADTFD outperforms other adaptive kernel and fixed kernel TFDs in terms of its ability to achieve high resolution for EEG seizure signals. It is also shown that the ADTFD can be used to define new time-frequency features that can lead to better classification of EEG signals, e.g. the use of the ADTFD leads to 97.5% total accuracy, which is by 2% more than the results achieved by the other methods.

EEG Analysis

2017 ◽  
Author(s):  
Fabrice Wendling ◽  
Marco Congendo ◽  
Fernando H. Lopes da Silva
Keyword(s):  
Matching Pursuit ◽  
Effective Connectivity ◽  
Eeg Signals ◽  
Model Decomposition ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Artifact Rejection ◽  
Transient Events

This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.

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