Search results for "Independent Component Analysis."
showing 10 items of 82 documents
Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening
2019
AbstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method t…
Individual Independent Component Analysis on EEG: Event-Related Responses Vs. Difference Wave of Deviant and Standard Responses
2016
Independent component analysis (ICA) is often used to spatially filter event-related potentials (ERPs). When an oddball paradigm is applied to elicit ERPs, difference wave (DW, responses of deviant stimuli minus those of standard ones) is often used to remove the common responses between the deviant and the standard. Thus, DW can be produced first, and then ICA is used to decompose the DW. Or, ICA is performed on responses of the deviant and standard stimuli separately, and then DW is applied on the filtered responses. In this study, we compared the two approaches to analyzing mismatch negativity (MMN). We found that DW introduced noise in the time and space domains, resulting in more diffi…
Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks
2019
Deep learning has contributed greatly to functional magnetic resonance imaging (fMRI) analysis, however, spatial maps derived from fMRI data by independent component analysis (ICA), as promising biomarkers, have rarely been directly used to perform individualized diagnosis. As such, this study proposes a novel framework combining ICA and convolutional neural network (CNN) for classifying schizophrenia patients (SZs) and healthy controls (HCs). ICA is first used to obtain components of interest which have been previously implicated in schizophrenia. Functionally informative slices of these components are then selected and labelled. CNN is finally employed to learn hierarchical diagnostic fea…
EEG Effective Source Projections Are More Bilaterally Symmetric in Infants Than in Adults
2020
Although anatomical brain hemispheric asymmetries have been clearly documented in the infant brain, findings concerning functional hemispheric specialization have been inconsistent. The present report aims to assess whether bilaterally symmetric synchronous activity between the two hemispheres is a characteristic of the infant brain. To asses cortical bilateral synchronicity, we used decomposition by independent component analysis (ICA) of high-density electroencephalographic (EEG) data collected in an auditory passive oddball paradigm. Decompositions of concatenated 64-channel EEG data epochs from each of 34 typically developing 6-month-old infants and from 18 healthy young adults particip…
Independent component analysis on the mismatch negativity in an uninterrupted sound paradigm.
2008
We compared the efficiency of the independent component analysis (ICA) decomposition procedure against the difference wave (DW) and optimal digital filtering (ODF) procedures in the analysis of the mismatch negativity (MMN). The comparison was made in a group of 54 children aged 8-16 years. The MMN was elicited in a passive oddball protocol presenting uninterrupted auditory stimulation consisting of two frequent alternating tones (600 and 800 Hz) of 100 ms duration each. Infrequently, one of the 600 Hz tones was shortened to 50 or 30 ms. The event related potentials (ERPs) were decomposed into the MMN-like and non-MMN-like independent components (ICs) through the FastICA algorithm. The ICA …
Event-related potentials to unattended changes in facial expressions: detection of regularity violations or encoding of emotions?
2013
Visual mismatch negativity (vMMN), a component in event-related potentials (ERPs), can be elicited when rarely presented “deviant” facial expressions violate regularity formed by repeated “standard” faces. vMMN is observed as differential ERPs elicited between the deviant and standard faces. It is not clear, however, whether differential ERPs to rare emotional faces interspersed with repeated neutral ones reflect true vMMN (i.e., detection of regularity violation) or merely encoding of the emotional content in the faces. Furthermore, a face-sensitive N170 response, which reflects structural encoding of facial features, can be modulated by emotional expressions. Owing to its similar latency …
Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance…
2019
Background. Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions. New method. The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popu…
Brain integrative function driven by musical training during real-world music listening
2017
The present research investigated differences in the brain dynamics of continuous, real-world music listening between listeners with and without professional musical training, using functional magnetic resonance imaging (fMRI). A replication study was aimed at validating the reliability of the naturalistic approach to studying brain responses to music, wherein the brain signal and the acoustic information extracted from the musical stimulus were correlated. After a successful replication, a series of three studies dealt with differences in integrative brain function during music listening between musicians and nonmusicians. Findings (a) emphasized the crucial role of the distinctive postura…
Unsupervised representation learning of spontaneous MEG data with nonlinear ICA
2023
Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but stru…
Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data
2015
An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the conventional GICA. The extended GICA resulted slightly faster ICA convergence and, more interestingly, extracted more stimulus-related components than its conventional counterpart. Therefore, we think the extension is beneficial enhancement for GICA, especially when applied to challenging fMRI d…