Empirical Mode Decomposition Algorithms for Classification of Single-Channel EEG Manifesting McGurk Effect

2020 ◽  
pp. 49-60
Author(s):  
Arup Kumar Pal ◽  
Dipanjan Roy ◽  
G. Vinodh Kumar ◽  
Bipra Chatterjee ◽  
L. N. Sharma ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Single Channel ◽  
Mcgurk Effect ◽  
Decomposition Algorithms ◽  
Mode Decomposition

FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 25 (06) ◽  
pp. 1350058 ◽  
Author(s):  
Pablo F. Diez ◽  
Vicente A. Mut ◽  
Eric Laciar ◽  
Abel Torres ◽  
Enrique M. Avila Perona
Keyword(s):  
Empirical Mode Decomposition ◽  
Communication System ◽  
Brain Activity ◽  
Features Extraction ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Machine Communication ◽  
Mental Tasks

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

Visualization, detection and classification of Risso's and Pacific white-sided dolphins using an empirical mode decomposition-based process

2020 ◽  
Vol 148 (4) ◽  
pp. 2766-2766
Author(s):  
Mahdi H. Al-Badrawi ◽  
Yue Liang ◽  
Nicholas J. Kirsch ◽  
Kerri D. Seger
Keyword(s):  
Empirical Mode Decomposition ◽  
Mode Decomposition
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