Validating online recursive independent component analysis on EEG data

2015 ◽  
Author(s):  
Sheng-Hsiou Hsu ◽  
Tim Mullen ◽  
Tzyy-Ping Jung ◽  
Gert Cauwenberghs
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Data

scholarly journals Independent Component Analysis and Source Localization on Mobile EEG Data Can Identify Increased Levels of Acute Stress

2017 ◽  
Vol 11 ◽  
Author(s):  
Bryan R. Schlink ◽  
Steven M. Peterson ◽  
W. D. Hairston ◽  
Peter König ◽  
Scott E. Kerick ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Source Localization ◽  
Acute Stress ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Data

scholarly journals Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data

2019 ◽  
Vol 15 (1) ◽  
pp. 1-12
Author(s):  
Ali Al-Saegh
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Speaker Identification ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Signals ◽  
Artifact Detection ◽  
Eeg Data ◽  
Ica Algorithm ◽  
Complex Valued

Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.

scholarly journals PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Kenneth Ball ◽  
Nima Bigdely-Shamlo ◽  
Tim Mullen ◽  
Kay Robbins
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Simulated Data ◽  
Real Data ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Data ◽  
Original Observation ◽  
Observation Space

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we callPairwise Complex Independent Component Analysis(PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.

Maximum Independent Component Analysis with Application to EEG Data

2020 ◽  
Vol 35 (1) ◽  
pp. 145-157
Author(s):  
Ruosi Guo ◽  
Chunming Zhang ◽  
Zhengjun Zhang
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Data

scholarly journals Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Suogang Wang ◽  
Christopher J. James
Keyword(s):  
Independent Component Analysis ◽  
Computational Cost ◽  
Rhythmic Activity ◽  
Component Analysis ◽  
Independent Component ◽  
Classification Rate ◽  
Eeg Data ◽  
Brain Computer Interfacing ◽  
Bci System ◽  
Computer Interfacing

We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system.

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