scholarly journals Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

2019 ◽  
Author(s):  
Yaqub Jonmohamadi ◽  
Suresh Muthukumaraswamy ◽  
Joseph Chen ◽  
Jonathan Roberts ◽  
Ross Crawford ◽  
...  
Keyword(s):  
Memory Task ◽  
Tensor Decomposition ◽  
Space Time ◽  
Fmri Data ◽  
Tensor Space ◽  
Time Frequency ◽  
Neuroscience Research ◽  
Neuroimaging Data ◽  
Source Level ◽  
The Individual

AbstractThe fusion of simultaneously recorded EEG and fMRI data is of great value to neuroscience research due to the complementary properties of the individual modalities. Traditionally, techniques such as PCA and ICA, which rely on strong strong non-physiological assumptions such as orthogonality and statistical independence, have been used for this purpose. Recently, tensor decomposition techniques such as parallel factor analysis have gained more popularity in neuroimaging applications as they are able to inherently contain the multidimensionality of neuroimaging data and achieve uniqueness in decomposition without imposing strong assumptions. Previously, the coupled matrix-tensor decomposition (CMTD) has been applied for the fusion of the EEG and fMRI. Only recently the coupled tensor-tensor decomposition (CTTD) has been proposed. Here for the first time, we propose the use of CTTD of a 4th order EEG tensor (space, time, frequency, and participant) and 3rd order fMRI tensor (space, time, participant), coupled partially in time and participant domains, for the extraction of the task related features in both modalities. We used both the sensor-level and source-level EEG for the coupling. The phase shifted paradigm signals were incorporated as the temporal initializers of the CTTD to extract the task related features. The validation of the approach is demonstrated on simultaneous EEG-fMRI recordings from six participants performing an N-Back memory task. The EEG and fMRI tensors were coupled in 9 components out of which 7 components had a high correlation (more than 0.85) with the task. The result of the fusion recapitulates the well-known attention network as being positively, and the default mode network working negatively time-locked to the memory task.

scholarly journals CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave

2016 ◽  
Author(s):  
Nikolaas N. Oosterhof ◽  
Andrew C. Connolly ◽  
James V. Haxby
Keyword(s):  
Open Source ◽  
State Of The Art ◽  
Data Representation ◽  
Space Time ◽  
Source Distribution ◽  
Fmri Data ◽  
Time Frequency ◽  
Link Type ◽  
Eeg Data ◽  
Multivariate Pattern

AbstractRecent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto-and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens.CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species.It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets.CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques.CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality.CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian.CoSMoMVPA is Free/Open Source Software under the permissive MIT license.Website: https://cosmomvpa.orgSource code: https://github.com/CoSMoMVPA/CoSMoMVPA

scholarly journals Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

2021 ◽  
Author(s):  
Wenya. Liu ◽  
Xiulin. Wang ◽  
Jing. Xu ◽  
Yi. Chang ◽  
Timo. Hämäläinen ◽  
...  
Keyword(s):  
Major Depression ◽  
Music Perception ◽  
Permutation Test ◽  
Tensor Decomposition ◽  
Brain Regions ◽  
Music Listening ◽  
Time Frequency ◽  
Oscillatory Networks ◽  
Coupled Constraints ◽  
The Individual

AbstractPrevious researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

Space-Time-Frequency Coding for MIMO Relay System Based on Tensor Decomposition

2020 ◽  
Vol 63 (2) ◽  
pp. 77-87
Author(s):  
Qingzhu Wang ◽  
Lijuan Zhang ◽  
Bin Li ◽  
Yihai Zhu
Keyword(s):  
Tensor Decomposition ◽  
Space Time ◽  
Relay System ◽  
Mimo Relay ◽  
Time Frequency ◽  
Frequency Coding

Nested Tucker tensor decomposition with application to MIMO relay systems using tensor space–time coding (TSTC)

2016 ◽  
Vol 128 ◽  
pp. 318-331 ◽  
Author(s):  
Gérard Favier ◽  
C. Alexandre R. Fernandes ◽  
André L.F. de Almeida
Keyword(s):  
Tensor Decomposition ◽  
Space Time ◽  
Tensor Space ◽  
Mimo Relay ◽  
Space Time Coding ◽  
Relay Systems ◽  
Time Coding

Improving Space-Time-Frequency MIMO-OFDM with ICI Self-Cancellation Scheme using Least Square Error Estimator

2015 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Nur Farahiah Ibrahim ◽  
Zahari Abu Bakar ◽  
Azlina Idris
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Symbol Error Rate ◽  
Space Time ◽  
Least Square ◽  
Error Estimator ◽  
Frequency Diversity ◽  
Time Frequency ◽  
Mimo Ofdm ◽  
Subcarrier Mapping

Channel estimation techniques for Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based on comb type pilot arrangement with least-square error (LSE) estimator was investigated with space-time-frequency (STF) diversity implementation. The frequency offset in OFDM effected its performance. This was mitigated with the implementation of the presented inter-carrier interference self-cancellation (ICI-SC) techniques and different space-time subcarrier mapping. STF block coding in the system exploits the spatial, temporal and frequency diversity to improve performance. Estimated channel was fed into a decoder which combined the STF decoding together with the estimated channel coefficients using LSE estimator for equalization. The performance of the system was compared by measuring the symbol error rate with a PSK-16 and PSK-32. The results show that subcarrier mapping together with ICI-SC were able to increase the system performance. Introduction of channel estimation was also able to estimate the channel coefficient at only 5dB difference with a perfectly known channel.

scholarly journals Space-Time-Frequency Code implementation in MB-OFDM UWB communications: Design criteria and performance

2009 ◽  
Vol 8 (2) ◽  
pp. 701-713 ◽  
Author(s):  
Le Chung Tran ◽  
Alfred Mertins
Keyword(s):  
Space Time ◽  
Design Criteria ◽  
Time Frequency ◽  
Frequency Code ◽  
And Performance

A Simple Space-Time-Frequency Block Code Scheme for Four Transmit Antennas

2012 ◽  
Vol 70 (4) ◽  
pp. 1363-1372
Author(s):  
Wei Song ◽  
S. P. Balakannan ◽  
Moon Ho Lee
Keyword(s):  
Block Code ◽  
Space Time ◽  
Code Scheme ◽  
Time Frequency
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