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Estimating neural sources from each time-frequency component of magnetoencephalographic data

K. Sekihara, S.S. Nagarajan, D. Poeppel, S. Miyauchi, N. Fujimaki, H. Koizumi, Y. Miyashita
2000 IEEE Transactions on Biomedical Engineering  
We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation.  ...  This method, referred to as the time-frequency multiple-signal-classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest.  ...  A part of the computer simulation reported in this paper used the Time-Frequency Toolbox provided by France's CNRS (Centre National de la Recherche Scientifique).  ... 
doi:10.1109/10.841336 pmid:10851808 fatcat:3kfolbxmhzbm7bhx6rvf3tkzpe

Estimating neural sources from each time-frequency component of magnetoencephalographic data

K. Sekihara, S. Nagarajan, D. Poeppel, Y. Miyashita
Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380)  
We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation.  ...  This method, referred to as the time-frequency multiple-signal-classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest.  ...  A part of the computer simulation reported in this paper used the Time-Frequency Toolbox provided by France's CNRS (Centre National de la Recherche Scientifique).  ... 
doi:10.1109/tfsa.1998.721363 fatcat:wut65ui24jblvpod3mdaztyru4

Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings

Ricardo Vigário, Veikko Jousmäki, Matti Hämäläinen, Riitta Hari, Erkki Oja
1997 Neural Information Processing Systems  
We have studied the application of an independent component analysis (ICA) approach to the identification and possible removal of artifacts from a magnetoencephalographic (MEG) recording.  ...  This statistical technique separates components according to the kurtosis of their amplitude distributions over time, thus distinguishing between strictly periodical signals, and regularly and irregularly  ...  Acknowledgment Supported by a grant from Junta Nacional de Investiga~ao Cientifica e Tecnologica, under its 'Programa PRAXIS XXI' (R.Y.) and the Academy of Finland (R.H.).  ... 
dblp:conf/nips/VigarioJHHO97 fatcat:hhssyltzifeddlnjcmp4c7ep2e

Magnetoencephalographic study of the cortical activity elicited by human voice

Atsuko Gunji, Sachiko Koyama, Ryouhei Ishii, Daniel Levy, Hidehiko Okamoto, Ryusuke Kakigi, Christo Pantev
2003 Neuroscience Letters  
The source locations of equivalent current dipoles for both components were estimated around the Heschl's gyrus in both hemispheres.  ...  The stimuli were sounds produced by four singers and four musical instruments at each of two fundamental frequencies: 220 Hz (musical note A3) and 261.9 Hz (C3).  ...  Acknowledgements This study was supported by the CIHR, the Ontario Innovation Trust and the Canadian Foundation for Innovation, a Grant-in-Aid for Scientific Research (07335), and the Ministry of Public  ... 
doi:10.1016/s0304-3940(03)00640-2 pmid:12893414 fatcat:3cm3yuveynf2fpsbn4mfp3vvpa

A magnetoencephalographic study of face processing: M170, gamma-band oscillations and source localization

Zaifeng Gao, Abraham Goldstein, Yuval Harpaz, Myriam Hansel, Elana Zion-Golumbic, Shlomo Bentin
2012 Human Brain Mapping  
Here we recorded MEG rather than EEG, assessed the sources of the M170 and Gamma oscillations using beamformer, and explored the sensitivity of these neural manifestations to global, featural and configural  ...  V C 2012 Wiley Periodicals, Inc. r A Magnetoencephalographic Study of Face Processing r r 3 r r A Magnetoencephalographic Study of Face Processing r r 9 r r A Magnetoencephalographic Study of Face Processing  ...  Sources of M170 The active windows used for estimating the sources of the M170 by SAMerf were 100 ms long, located during the time course of the MEG so that they included the peak of the M170 component  ... 
doi:10.1002/hbm.22028 pmid:22422432 pmcid:PMC3382029 fatcat:wistyyjue5denl75x52skpkte4

Commentary: Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach

Esther Florin, Sylvain Baillet
2018 Frontiers in Computational Neuroscience  
SB was supported by a Discovery Grant from the National Science and Engineering Research Council of Canada and the NIH (2R01EB009048-05).  ...  FUNDING EF gratefully acknowledges support from the Volkswagen Foundation (89387).  ...  MISCHARACTERIZATION OF TECHNICAL DIFFERENCES Artifact Removal From GEA's statement that at least 10 independent components were removed from the data, it seems that they used their own preprocessing  ... 
doi:10.3389/fncom.2018.00026 pmid:29713271 pmcid:PMC5911466 fatcat:eii43te25bfujde7p2u7z3wk6e

Neural activity involved in the perception of human and meaningful object motion

Naznin Virji-Babul, Teresa Cheung, Daniel Weeks, Kimberly Kerns, Maggie Shiffrar
2007 NeuroReport  
Time courses of grand-mean source estimates were computed and time frequency maps were calculated. For both conditions, activity began in the posterior occipital and mid-parietal areas.  ...  We characterized magnetoencephalographic responses during observation of point-light displays of human and object motion.  ...  This 15-source model was applied to the group average data and resulted in a time course of source strength at each regional source for each condition.  ... 
doi:10.1097/wnr.0b013e32821c5470 pmid:17589311 fatcat:6dot3rlrjrf6jnrm3354mstexq

Time-frequency MEG-MUSIC algorithm

K. Sekihara, S. Nagarajan, D. Poeppel, Y. Miyashita
1999 IEEE Transactions on Medical Imaging  
Accordingly, the method allows us to estimate the locations of neural sources from each time-frequency component.  ...  We propose a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation.  ...  The method combines time-frequency analysis [2] with the MEG multiple-signal-classification (MUSIC) algorithm [3] and allows estimation of neural sources from each time-frequency component.  ... 
doi:10.1109/42.750262 pmid:10193700 fatcat:bvjprhqskfgmtb6cuujqwanc4u

Magnetocardiogram interference in magnetoencephalographic data: its importance in cases of interictal epileptic data

P.D. Bamidis
2003 Computers in Cardiology, 2003  
These are time depthplots of source activity and global field power measures in association with ECG traces. Finally, possible ECG artifact rejection methods are discussed.  ...  A distributed source modelling technique of brain activity, namely, Magnetic Field Tomography (MFT) provides the context of the methodology used to certify that results obtained are free from cardiac interferences  ...  In addition to this, MFT estimates are integrated over the area of each source space level and for each time slice separately.  ... 
doi:10.1109/cic.2003.1291224 fatcat:h5o7eci52rduhnnwpavus3us3m

Visualization of Magnetoencephalographic Data Using Minimum Current Estimates

K. Uutela, M. Hämäläinen, E. Somersalo
1999 NeuroImage  
The locations of active brain areas can be estimated from the magnetic field the neural current sources produce.  ...  In this work we study a visualization method of magnetoencephalographic data that is based on minimum l 1 -norm estimates.  ...  No assumptions about the activation sequence are made, and the time course of each source can also be estimated.  ... 
doi:10.1006/nimg.1999.0454 pmid:10417249 fatcat:6rcbtcbxdfcbpgmcnopxxa65le

Semi-automated brain responses in communication: A magnetoencephalographic hyperscanning study

Kazuyoshi Takano, Hayato Watanabe, Kazuyori Yagyu, Atsushi Shimojo, Jared Boasen, Yui Murakami, Hideaki Shiraishi, Koichi Yokosawa, Takuya Saito
2020 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)  
Each individual in each pair viewed a combined 80 randomized 20 s trials of 40 real-time and 40 recorded (hereafter, real and simulated, respectively) videos of the opposite party's face.  ...  To accurately assess the neural processing underlying these interactions, synchronous and simultaneous recording of the brain activity from both parties is needed, a method known as hyperscanning.  ...  The passband frequency of each device ranged from 0.1 to 200 Hz, and signals were sampled at 600 Hz.  ... 
doi:10.1109/embc44109.2020.9176538 pmid:33018611 fatcat:6glp7i2u2zhkvh2lpajwjlfhhm

Adaptive neural network classifier for decoding MEG signals [article]

Ivan Zubarev, Rasmus Zetter, Hanna-Leena Halme, Lauri Parkkonen
2019 arXiv   pre-print
Here, we introduce a CNN optimized for classification of brain states from magnetoencephalographic (MEG) measurements.  ...  We show here that the proposed network is able to decode event-related responses as well as modulations of oscillatory brain activity and that it outperforms more complex neural networks and traditional  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations.  ... 
arXiv:1805.10981v2 fatcat:c256anxl3bbnxpd7nurazwpyle

Splitting of the magnetic encephalogram into «brain» and «non-brain» physiological signals based on the joint analysis of frequency-pattern functional tomograms and magnetic resonance images

Rodolfo R. Llinás, Stanislav Rykunov, Kerry D. Walton, Anna Boyko, Mikhail Ustinin
2022 Frontiers in Neural Circuits  
The article considers the problem of dividing the encephalography data into two time series, that generated by the brain and that generated by other electrical sources located in the human head.  ...  In this method, one spatial position is assigned to each frequency component. Magnetic resonance images of the head were evaluated to annotate the space to be included in the analysis.  ...  magnetoencephalographic data into two synthetic encephalograms: one originating from the brain, another originating from the non-brain physiological sources once the non-physiological sources are eliminated  ... 
doi:10.3389/fncir.2022.834434 pmid:36092277 pmcid:PMC9458866 fatcat:cgn4xytlfzf6bbekcjqukvshka

External noise removed from magnetoencephalographic signal using Independent Component Analyses of reference channels [article]

Jeff Hanna, Cora Kim, Nadia Müller-Voggel
2020 arXiv   pre-print
Many magnetoencephalographs (MEG) contain, in addition to data channels, a set of reference channels positioned relatively far from the head that provide information on magnetic fields not originating  ...  We present two algorithms for identifying and removing such noise components from the data which can in many cases significantly improve data quality.  ...  Acknowledgements We would like to thank Antonia Keck, Denise Kunze, and Carolin Spielau-Romer for assistance in data collection, and Martin Kaltenhäuser, Stefan Rampp, Eric Larson, and two anonymous reviewers  ... 
arXiv:2001.03397v1 fatcat:bs6tnj275jb7toa2uaets2nnsa

A New Method to Identify Multiple Sources of Oscillatory Activity from Magnetoencephalographic Data

Ole Jensen, Simo Vanni
2002 NeuroImage  
The main advantage of the proposed method is that it provides an efficient approach for simultaneous estimation of multiple sources of oscillatory activity in the same frequency band. © 2002 Elsevier Science  ...  In this work we introduce a convenient method for source localization using minimum current estimates in the frequency domain.  ...  It is in principle possible to perform a minimum current estimate of oscillatory neural activity in the time domain after filtering the data in the frequency band of interested.  ... 
doi:10.1006/nimg.2001.1020 pmid:11848699 fatcat:2bgsuqfdhnc4bihdq3sfh7d7da
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