scholarly journals Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3297
Author(s):  
Tat’y Mwata-Velu ◽  
Juan Gabriel Avina-Cervantes ◽  
Jorge Mario Cruz-Duarte ◽  
Horacio Rostro-Gonzalez ◽  
Jose Ruiz-Pinales
Keyword(s):  
Empirical Mode Decomposition ◽  
Short Term Memory ◽  
Sequence Classification ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Noisy Signals ◽  
Public Dataset ◽  
The Right ◽  
Fold Cross Validation

Motor Imagery Electroencephalogram (MI-EEG) signals are widely used in Brain-Computer Interfaces (BCI). MI-EEG signals of large limbs movements have been explored in recent researches because they deliver relevant classification rates for BCI systems. However, smaller and noisy signals corresponding to hand-finger imagined movements are less frequently used because they are difficult to classify. This study proposes a method for decoding finger imagined movements of the right hand. For this purpose, MI-EEG signals from C3, Cz, P3, and Pz sensors were carefully selected to be processed in the proposed framework. Therefore, a method based on Empirical Mode Decomposition (EMD) is used to tackle the problem of noisy signals. At the same time, the sequence classification is performed by a stacked Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed method was evaluated using k-fold cross-validation on a public dataset, obtaining an accuracy of 82.26%.

scholarly journals Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study

2018 ◽  
Vol 12 ◽  
Author(s):  
Pablo Andrés Muñoz-Gutiérrez ◽  
Eduardo Giraldo ◽  
Maximiliano Bueno-López ◽  
Marta Molinas
Keyword(s):  
Comparative Study ◽  
Empirical Mode Decomposition ◽  
Eeg Signals ◽  
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.

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