• Corpus ID: 36564021

Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data

@article{Parra2018CorrelatedCA,
  title={Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data},
  author={Lucas C. Parra and Stefan Haufe and Jacek P Dmochowski},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.08881}
}
How does one find dimensions in multivariate data that are reliably expressed across repetitions? For example, in a brain imaging study one may want to identify combinations of neural signals that are reliably expressed across multiple trials or subjects. For a behavioral assessment with multiple ratings, one may want to identify an aggregate score that is reliably reproduced across raters. Correlated Components Analysis (CorrCA) addresses this problem by identifying components that are… 
Multiway canonical correlation analysis of brain data
TLDR
Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all.
Multi-set Canonical Correlation Analysis simply explained.
There are a multitude of methods to perform multi-set correlated component analysis (MCCA), including some that require iterative solutions. The methods differ on the criterion they optimize and the
Analysis of Correlation in Neural Responses across Multiple Subjects or Trials during Decision-making for Newsvendor Problem
TLDR
The alpha-band power results for different decision-making phases affirmed that the difficulty of different test phases modulates subjects’ engagement, and confirmed the modulation of the engagement state on the correlations of the neural activity across multiple subjects or repeated trials.
Characterizing neural phase-space trajectories via Principal Louvain Clustering
TLDR
Principal Louvain Clustering (PLC) is proposed, to identify clusters in a low-dimensional data subspace, based on time-varying trajectories of spectral dynamics across multisite local field potential recordings in awake behaving mice.
Similar cognitive processing synchronizes brains, hearts, and eyes
TLDR
It is shown that physiological synchrony requires only two things, namely, effective cognitive processing of a common stimulus, and a robust coupling between brain activity and the physiological signal in question, and is confirmed for heart rate, pupil size, gaze position and saccade rate.
Narrowband multivariate source separation for semi-blind discovery of experiment contrasts
TLDR
A feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences, and a novel adaptation that extends the power of GED in cognitive electrophysiology.
Inter-subject correlations during natural viewing: A filter-bank approach*
TLDR
It is demonstrated that ISCs of neural activation as measured by electroencephalogram (EEG) recordings are influenced significantly by non-neural artifacts such as occulograms.
Narrowband multivariate source separation for semi-blind discovery of experiment contrasts
TLDR
A feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences and a novel adaptation that extends the power of GED in cognitive electrophysiology.
Cognitive processing of a common stimulus synchronizes brains, hearts, and eyes
TLDR
The results suggest that inter-subject correlation is the result of similar cognitive processing of a shared stimulus and thus emerges only for those signals that exhibit a robust brain-body connection.
Natural Music Evokes Correlated EEG Responses Reflecting Temporal Structure and Beat
TLDR
It is shown that natural music evokes significant inter-subject and stimulus-response correlations, and it is suggested that the neural correlates of musical engagement may be distinct from those of enjoyment.
...
1
2
3
4
...

References

SHOWING 1-10 OF 83 REFERENCES
Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data
TLDR
It is demonstrated that simple extensions of TRCA can provide most distinctive signals for two tasks and can integrate multiple modalities of information to remove task-unrelated artifacts.
Multiview Bayesian Correlated Component Analysis
TLDR
A hierarchical probabilistic model is proposed that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis.
Power‐law dynamics in neuronal and behavioral data introduce spurious correlations
TLDR
It is shown that it is possible to "predict" reaction times from one subject on the basis of EEG activity recorded in another subject simply owing to the fact that both measures display power‐law dynamics.
Multiway canonical correlation analysis of brain data
TLDR
Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all.
Inter‐subject alignment of MEG datasets in a common representational space
TLDR
This article investigated an alternative method that bypasses source‐localization and analyzes the sensor recordings themselves and aligns their temporal signatures across subjects, and used a multivariate approach, multiset canonical correlation analysis (M‐CCA), to transform individual subject data to a low‐dimensional common representational space.
Linear Spatial Integration for Single-Trial Detection in Encephalography
TLDR
This work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy.
Intersubject consistency of cortical MEG signals during movie viewing
TLDR
The capability of the proposed methodology to uncover cortical MEG signatures from single-trial signals that are consistent across spectators of a movie is demonstrated, as well as the brain origin of the across-subjects correlated signals.
Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework
TLDR
The results show that gCCA is an efficient approach for extracting the default mode network, assessing brain connectivity, and processing event-related and resting-state datasets in which the temporal BOLD signal varies from subject to subject.
Multiway Canonical Correlation Analysis of Brain Signals
TLDR
Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all.
On the interpretation of weight vectors of linear models in multivariate neuroimaging
TLDR
It is demonstrated that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study, in contrast to the interpretation of backward model parameters.
...
1
2
3
4
5
...