Data model conversion for independent component analysis to extract brain signals

2011 ◽  
Author(s):  
Fengyu Cong ◽  
Tapani Ristaniemi
Keyword(s):  
Independent Component Analysis ◽  
Data Model ◽  
Component Analysis ◽  
Independent Component ◽  
Brain Signals ◽  
Model Conversion

LOCAL LINEAR INDEPENDENT COMPONENT ANALYSIS BASED ON CLUSTERING

2000 ◽  
Vol 10 (06) ◽  
pp. 439-451 ◽  
Author(s):  
JUHA KARHUNEN ◽  
SIMONA MĂlĂROIU ◽  
MIKA ILMONIEMI
Keyword(s):  
Independent Component Analysis ◽  
Data Model ◽  
Component Analysis ◽  
Independent Component ◽  
Self Organizing Maps ◽  
Crude Approximation ◽  
Data Grouping ◽  
Grouping Algorithm ◽  
Dense Representation ◽  
Better Than

In standard Independent Component Analysis (ICA), a linear data model is used for a global description of the data. Even though linear ICA yields meaningful results in many cases, it can provide a crude approximation only for general nonlinear data distributions. In this paper a new structure is proposed, where local ICA models are used in connection with a suitable grouping algorithm clustering the data. The clustering part is responsible for an overall coarse nonlinear representation of the data, while linear ICA models of each cluster are used for describing local features of the data. The goal is to represent the data better than in linear ICA while avoiding computational difficulties related with nonlinear ICA. Several data grouping methods are considered, including standard K-means clustering, self-organizing maps, and neural gas. Connections to existing methods are discussed, and experimental results are given for artificial data and natural images. Furthermore, a general theoretical framework encompassing a large number of methods for representing data is introduced. These range from global, dense representation methods to local, very sparse coding methods. The proposed local ICA methods lie between these two extremes.

scholarly journals Session to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals

2019 ◽  
Vol 15 (1) ◽  
pp. 13-27
Author(s):  
Zaineb Alhakeem ◽  
Ramzy Ali
Keyword(s):  
Independent Component Analysis ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Component Analysis ◽  
Independent Component ◽  
Learning Method ◽  
Common Spatial Pattern ◽  
Brain Signals ◽  
The Brain ◽  
Better Than

Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.

scholarly journals A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

2017 ◽  
Vol 2 (1) ◽  
pp. 9
Author(s):  
Wahyu Caesarendra
Keyword(s):  
Independent Component Analysis ◽  
Brain Activity ◽  
Lie Detection ◽  
Component Analysis ◽  
Independent Component ◽  
Non Invasive ◽  
Brain Signals ◽  
Multiple Electrodes ◽  
Almost All

The progress of today's technology is growing very quickly. This becomes the motivation for the community to be able to continue and provide innovations. One technology to be developed is the application of brain signals or called with electroencephalograph (EEG). EEG is a non-invasive measurement method that represents electrical signals from brain activity obtained by placement of multiple electrodes on the scalp in the area of the brain, thus obtaining information on electrical brain signals to be processed and analyzed. Lie is an act of covering up something so that only the person who is lying knows the truth of the statement. The hidden information from lying subjects will elicit an EEG-P300 signal response using Independent Component Analysis (ICA) in different shapes of amplitude that tends to be larger around 300 ms after stimulation. The method used in the experiment is to invite subject in a card game so that the process can be done naturally and the subject can well stimulated. After the trials there are several results almost all subjects have the same frequency on the frequency of 24-27 Hz. This is a classification of beta waves that have a frequency of 13-30 Hz where the beta wave is closely related to active thinking and attention, focusing on the outside world or solving concrete problems.

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