scholarly journals Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

2017 ◽  
Author(s):  
Sebastian Michelmann ◽  
Matthias S. Treder ◽  
Benjamin Griffiths ◽  
Casper Kerrén ◽  
Frédéric Roux ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Real Data ◽  
Component Analysis ◽  
Independent Component ◽  
Electrode Spacing ◽  
Noise Sources ◽  
Linear Superposition ◽  
Depth Electrodes

AbstractIntracranial recordings from patients implanted with depth electrodes are a valuable source of information in cognitive neuroscience. They allow for the unique opportunity to record brain activity with a high spatial and temporal resolution. To extract the local signal of interest in stereotactic EEG (S-EEG) data, a common pre-processing choice is to re-reference the data with a bipolar montage.With bipolar reference, each channel is subtracted from its neighbour in order to reduce commonalities between channels and isolate activity that is spatially confined. We here challenge the assumption that bipolar reference can effectively perform this task. We argue that in order to extract local activity, the distribution of the signal source of interest, as well as the distribution of interfering distant signals and noise sources need to be considered. Those can have a variable spatial extent and are modulated by electrode spacing, location and anatomical characteristics. Those factors are not accounted for by a fixed referencing scheme and bipolar reference can therefore not only decrease the signal to noise ratio (SNR) of the data, but also lead to mislocalization of activity and consequently to misinterpretation of results.We promote the perspective of regarding referencing as a spatial filtering operation with fixed coefficients. As an alternative, we propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. We argue that ICA performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and can therefore be used to identify activity that is local. We first describe and demonstrate this procedure on human S-EEG recordings. In a simulation with real data, we then quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when it comes to revealing local time series from the superposition of neighbouring channels.

scholarly journals Application of independent component analysis to the iKAGRA data

2020 ◽  
Vol 2020 (5) ◽  
Author(s):  
T Akutsu ◽  
M Ando ◽  
K Arai ◽  
Y Arai ◽  
S Araki ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Gravitational Wave ◽  
Signal To Noise Ratio ◽  
Correlation Method ◽  
Real Data ◽  
Component Analysis ◽  
Independent Component ◽  
Strongly Correlated ◽  
Wave Detector ◽  
First Time

Abstract We apply independent component analysis (ICA) to real data from a gravitational wave detector for the first time. Specifically, we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates the usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal results for the case of environmental noise acting on the strain channel linearly.

scholarly journals Identification of Automotive Seat Rattle Noise Using an Independent Component Analysis-Based Coherence Analysis Technique

2020 ◽  
Vol 10 (20) ◽  
pp. 7027
Author(s):  
Kookhyun Yoo ◽  
Un-Chang Jeong
Keyword(s):  
Independent Component Analysis ◽  
Evaluation Method ◽  
Signal Amplitude ◽  
Component Analysis ◽  
Independent Component ◽  
Coherence Analysis ◽  
Evaluation Technique ◽  
Noise Sources ◽  
Original Source ◽  
Analysis Technique

This study proposed a contribution evaluation through the independent component analysis (ICA) method. The necessity of applying ICA to the evaluation of contribution was investigated through numerical simulation. Moreover, the estimation of the number of input sources, the labeling of signals, and the restoration of the signal amplitude were considered to perform the ICA-based coherence evaluation. The contribution evaluation was performed using the coherence evaluation method and by applying the established ICA-based coherence evaluation method to the seat rattle noise of the vehicle. According to the result of the evaluation, with the coherence evaluation technique it was difficult to calculate the contribution in identifying noise sources that overlap in both spatially and in frequency, because it was challenging to distinguish between the two measured signals. By contrast, the ICA-based coherence evaluation was able to restore the original source and investigate the contribution.

Application of Fault Diagnosis Method Based on cICA to Gearbox

2014 ◽  
Vol 664 ◽  
pp. 148-152
Author(s):  
Shuang Xi Jing ◽  
Song Tao Guo ◽  
Jun Fa Leng ◽  
Xing Yu Zhao
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Early Application ◽  
Independent Components ◽  
Gear Fault ◽  
Constrained Independent Component Analysis ◽  
Non Gaussian

Constrained independent component analysis (cICA) is a new theory and new method derived from the independent component analysis (ICA).It can extract the desired independent components (ICs) from the data based on some prior information, thus overcoming the uncertainty of the traditional ICA. Early gearbox fault signals is often very weak ,characterized by non-Gaussian,low signal-to-noise ratio (SNR), which make the existing diagnosis methods in the diagnosis of early application restricted. In this paper,cICA algorithm is applied to gear fault diagnosis. Through the case studies verify the feasibility of this method to extract the desired independent components (ICs), indicating the applicability and effectiveness of the method.

Blind Separation of Excavator Noise Signals in Frequency Domain

2011 ◽  
Vol 105-107 ◽  
pp. 723-728
Author(s):  
Li Da Liao ◽  
Qing Hua He ◽  
Zhong Lin Hu
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Separation ◽  
Noise Sources ◽  
Amplitude Spectra ◽  
Complex Valued

In order to identify noise sources of an excavator in non-library environment, a complex-valued algorithm in frequency domain was applied. Firstly, an acoustic camera was used to acquire excavator’s noise signals, which were convolutive mixtures in time domain interfered by echo. Secondly, signals in time domain transformed into frequency domain by FT, turned to be complex-valued mixtures. Then, independent components of noise signals were obtained through separation of complex-valued mixtures using complex-valued algorithm based on independent component analysis. Finally, according to noise of diesel with muffler was mainly consist of surface noise, the relationship between principal frequencies and structrual parts was founded by comparing frequency-amplitude spectra and modal analysis in Ansys. Research shows that complex-valued algorithm based on fast fixed-point independent component analysis can effectively separate noise signals from an excavator in time domain, and noise sources can be well ascertained by comparing the modal analysis with blind separation components.

Dimension Selection for Feature Selection and Dimension Reduction with Principal and Independent Component Analysis

2007 ◽  
Vol 19 (2) ◽  
pp. 513-545 ◽  
Author(s):  
Inge Koch ◽  
Kanta Naito
Keyword(s):  
Independent Component Analysis ◽  
Dimension Reduction ◽  
Dimensional Space ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Independent Component ◽  
Data Sets ◽  
Optimal Dimension ◽  
Lower Dimensional Space

This letter is concerned with the problem of selecting the best or most informative dimension for dimension reduction and feature extraction in high-dimensional data. The dimension of the data is reduced by principal component analysis; subsequent application of independent component analysis to the principal component scores determines the most nongaussian directions in the lower-dimensional space. A criterion for choosing the optimal dimension based on bias-adjusted skewness and kurtosis is proposed. This new dimension selector is applied to real data sets and compared to existing methods. Simulation studies for a range of densities show that the proposed method performs well and is more appropriate for nongaussian data than existing methods.

SN Ratio Estimation and Speech Segment Detection of Extracted Signals Through Independent Component Analysis

2010 ◽  
Vol 14 (4) ◽  
pp. 364-374
Author(s):  
Takeshi Koya ◽  
◽  
Nobuo Iwasaki ◽  
Takaaki Ishibashi ◽  
Go Hirano ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Real World ◽  
Transfer Functions ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Snr Estimation ◽  
Speech Segment ◽  
Speech Segments ◽  
Source Signals

In real world environments where acoustic signals are contaminated with various noises, it is difficult to estimate the Signal-to-Noise Ratio (SNR) only from signals observed at microphones; the knowledge of acoustic transfer functions and original source signals is inevitable for SNR estimation. The present paper proposes a method to estimate SNR approximately in the real world environments without the knowledge of transfer functions and source signals: SNR is estimated after application of Independent Component Analysis (ICA) to the signals observed at microphones. Our proposed method also works as a speech segment detector since detection of speech segments are necessarily carried out in the course of SNR estimation. From several experimental results, the proposed method has been confirmed to be valid.

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.

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