Entropy-Based Relevance Selection of Independent Components Supporting Motor Imagery Tasks

2018 ◽  
pp. 359-367
Author(s):  
David Luna-Naranjo ◽  
David Cárdenas-Peña ◽  
Germán Castellanos-Dominguez
Keyword(s):  
Motor Imagery ◽  
Independent Components ◽  
Selection Of

scholarly journals Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Naeem ◽  
Clemens Brunner ◽  
Gert Pfurtscheller
Keyword(s):  
Motor Imagery ◽  
Visual Inspection ◽  
Full Range ◽  
Principal Component ◽  
Spatial Filters ◽  
Independent Components ◽  
Components Analysis ◽  
The Brain ◽  
Variance Criterion ◽  
Selection Of

The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centro-parietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.

Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis

2007 ◽  
Vol 28 (8) ◽  
pp. 957-964 ◽  
Author(s):  
Clemens Brunner ◽  
Muhammad Naeem ◽  
Robert Leeb ◽  
Bernhard Graimann ◽  
Gert Pfurtscheller
Keyword(s):  
Motor Imagery ◽  
Spatial Filtering ◽  
Independent Components Analysis ◽  
Independent Components ◽  
Eeg Data ◽  
Components Analysis ◽  
Selection Of

Discontinuous Adventitious Sounds Imaging by Semiautomatic Selection of Independent Components

2015 ◽  
pp. 631-634
Author(s):  
S. Charleston-Villalobos ◽  
N. Castañeda-Villa ◽  
R. González-Camarena ◽  
M. Mejía-Ávila ◽  
T. Aljama-Corrales
Keyword(s):  
Independent Components ◽  
Selection Of

scholarly journals Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata

2013 ◽  
Vol 52 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Abhronil Sengupta ◽  
Tathagatha Chakraborti ◽  
Amit Konar ◽  
D. N. Tibarewala
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Motor Imagery ◽  
Learning Automata ◽  
Eeg Signals ◽  
Selection Of ◽  
Automatic Feature Selection
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