Example for Applying the COMSTAT Multimodal Factor Analysis Algorithm to EEG Data to Describe Variance Sources

1983 ◽  
Vol 10 (2-3) ◽  
pp. 164-172 ◽  
Author(s):  
W.M. Herrmann ◽  
J. Röhmel ◽  
B. Streitberg ◽  
J. Willmann
Keyword(s):  
Factor Analysis ◽  
Analysis Algorithm ◽  
Eeg Data

Based on Distance Correlation of Shipment Inspection Data Multiple Factor Analysis Algorithm Research

2013 ◽  
Vol 475-476 ◽  
pp. 987-990
Author(s):  
Lin Niu ◽  
Jin Xin Huang ◽  
Jie Zhan ◽  
Jin Tao Cui
Keyword(s):  
Factor Analysis ◽  
Electric Power ◽  
Multiple Factor Analysis ◽  
Related Factors ◽  
Analysis Algorithm ◽  
Distance Correlation ◽  
Factors Influencing ◽  
Inspection Data ◽  
Transformer Fault ◽  
Multiple Factor

To analyze the main causes of electric power equipments failure, this paper based on distance correlation definition, proposed a method about correlation identification between the multi-factor and equipments failure. Firstly, this paper sets the related factors influencing the equipments failure as numerical value, based on distance correlation definition, calculate the correlation between each factors and equipments failure, so as to determine the power of the main causes of equipments failure. At last, through 220 kV transformer fault case validates the effectiveness of the proposed method.

scholarly journals A Comparison of Non-negative Tucker Decomposition and Parallel Factor Analysis for Identification and Measurement of Human EEG Rhythms

2020 ◽  
Vol 20 (3) ◽  
pp. 126-138 ◽  
Author(s):  
Zuzana Rošt’áková ◽  
Roman Rosipal ◽  
Saman Seifpour ◽  
Leonardo Jose Trejo
Keyword(s):  
Factor Analysis ◽  
Tensor Decomposition ◽  
Parallel Factor Analysis ◽  
Brain Disorder ◽  
Compact Structure ◽  
Number Of Factors ◽  
Eeg Data ◽  
Parallel Factor ◽  
Sensorimotor Rhythms ◽  
The Brain

AbstractAnalysis of changes in the brain neural electrical activity measured by the electroencephalogram (EEG) plays a crucial role in the area of brain disorder diagnostics. The elementary latent sources of the brain neural activity can be extracted by a tensor decomposition of continuously recorded multichannel EEG. Parallel factor analysis (PARAFAC) is a powerful approach for this purpose. However, the assumption of the same number of factors in each dimension of the PARAFAC model may be restrictive when applied to EEG data. In this article we discuss the potential benefits of an alternative tensor decomposition method – the Tucker model. We analyze situations, where in comparison to the PARAFAC solution, the Tucker model provides a more parsimonious representation of the EEG data decomposition. We show that this more parsimonious representation of EEG is achieved without reducing the ability to explain variance. We analyze EEG records of two patients after ischemic stroke and we focus on the extraction of specific sensorimotor oscillatory sources associated with motor imagery during neurorehabilitation training. Both models provided consistent results. The advantage of the Tucker model was a compact structure with only two spatial signatures reflecting the expected lateralized activation of the detected subject-specific sensorimotor rhythms.

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