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Journal articles on the topic 'EEG/MEG data'

Author: Grafiati

Published: 7 December 2024

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1

Litvak, Vladimir, Jérémie Mattout, Stefan Kiebel, Christophe Phillips, Richard Henson, James Kilner, Gareth Barnes, et al. "EEG and MEG Data Analysis in SPM8." Computational Intelligence and Neuroscience 2011 (2011): 1–32. http://dx.doi.org/10.1155/2011/852961.

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SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

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2

Gramfort, Alexandre, Martin Luessi, Eric Larson, Denis A. Engemann, Daniel Strohmeier, Christian Brodbeck, Lauri Parkkonen, and Matti S. Hämäläinen. "MNE software for processing MEG and EEG data." NeuroImage 86 (February 2014): 446–60. http://dx.doi.org/10.1016/j.neuroimage.2013.10.027.

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3

Hong, J., and S. C. Jun. "Single-trial Analysis for MEG/EEG spatiotemporal data." NeuroImage 47 (July 2009): S145. http://dx.doi.org/10.1016/s1053-8119(09)71470-3.

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4

Maris, Eric, and Robert Oostenveld. "Nonparametric statistical testing of EEG- and MEG-data." Journal of Neuroscience Methods 164, no. 1 (August 2007): 177–90. http://dx.doi.org/10.1016/j.jneumeth.2007.03.024.

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5

Gjini, Klevest, Susan M. Bowyer, Frank Wang, and Nash N. Boutros. "Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data." Clinical EEG and Neuroscience 51, no. 1 (August 4, 2019): 34–44. http://dx.doi.org/10.1177/1550059419867561.

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This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.

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6

Zhang, Junpeng, Sarang S. Dalal, Srikantan S. Nagarajan, and Dezhong Yao. "COHERENT MEG/EEG SOURCE LOCALIZATION IN TRANSFORMED DATA SPACE." Biomedical Engineering: Applications, Basis and Communications 22, no. 05 (October 2010): 351–65. http://dx.doi.org/10.4015/s1016237210002110.

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In some cases, different brain regions give rise to strongly-coherent electrical neural activities. For example, pure tone evoked activations of the bilateral auditory cortices exhibit strong coherence. Conventional 2nd order statistics-based spatio-temporal algorithms, such as MUSIC (MUltiple SIgnal Classification) and beamforming encounter difficulties in localizing such activities. In this paper, we proposed a novel solution for this case. The key idea is to map the measurement data into a new data space through a transformation prior to the localization. The orthogonal complement of the lead field matrix for the region to be suppressed is generated as the transformation matrix. Using a priori knowledge or another independent imaging method, such as sLORETA (standard LOw REsolution brain electromagnetic TomogrAphy), the coherent source regions can be primarily identified. And then, in the transformed data space a conventional spatio-temporal method, such as MUSIC, can be used to accomplish the localization of the remaining coherent sources. Repeatedly applying the method will achieve localization of all the coherent sources. The algorithm was validated by simulation experiments as well as by the reconstructions of real bilateral auditory cortical coherent activities.

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Jas, Mainak, Denis A. Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. "Autoreject: Automated artifact rejection for MEG and EEG data." NeuroImage 159 (October 2017): 417–29. http://dx.doi.org/10.1016/j.neuroimage.2017.06.030.

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8

Trujillo-Barreto, N. J., E. Martínez-Montes, P. A. Valdés-Sosa, and L. Melie-García. "Bayesian model for EEG/MEG and fMRI data fusion." NeuroImage 13, no. 6 (June 2001): 270. http://dx.doi.org/10.1016/s1053-8119(01)91613-1.

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9

Roś, Beata P., Fetsje Bijma, Mathisca C. M. de Gunst, and Jan C. de Munck. "A three domain covariance framework for EEG/MEG data." NeuroImage 119 (October 2015): 305–15. http://dx.doi.org/10.1016/j.neuroimage.2015.06.020.

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10

Kozinska, D., F. Carducci, and K. Nowinski. "Automatic alignment of EEG/MEG and MRI data sets." Clinical Neurophysiology 112, no. 8 (August 2001): 1553–61. http://dx.doi.org/10.1016/s1388-2457(01)00556-9.

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11

Nolte, G., F. Shahbazi Avarvand, and A. Ewald. "Localizing interacting brain activity from EEG and MEG data." International Journal of Psychophysiology 85, no. 3 (September 2012): 347. http://dx.doi.org/10.1016/j.ijpsycho.2012.06.152.

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12

Marzetti, Laura, Federico Chella, Vittorio Pizzella, and Guido Nolte. "Disentangling coupled brain systems from EEG and MEG data." International Journal of Psychophysiology 108 (October 2016): 8. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.024.

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13

Bruña, Ricardo, Delshad Vaghari, Andrea Greve, Elisa Cooper, Marius O. Mada, and Richard N. Henson. "Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration." Bioengineering 9, no. 10 (October 21, 2022): 591. http://dx.doi.org/10.3390/bioengineering9100591.

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Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this “face-trimming” method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.

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14

Horwitz, Barry, and David Poeppel. "How can EEG/MEG and fMRI/PET data be combined?" Human Brain Mapping 17, no. 1 (July 29, 2002): 1–3. http://dx.doi.org/10.1002/hbm.10057.

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15

Wu, Huanqi, Ruonan Wang, Yuyu Ma, Xiaoyu Liang, Changzeng Liu, Dexin Yu, Nan An, and Xiaolin Ning. "Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data." Bioengineering 11, no. 6 (June 13, 2024): 609. http://dx.doi.org/10.3390/bioengineering11060609.

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Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience.

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16

Opoku, Eugene A., Syed Ejaz Ahmed, Yin Song, and Farouk S. Nathoo. "Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data." Entropy 23, no. 3 (March 11, 2021): 329. http://dx.doi.org/10.3390/e23030329.

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Electroencephalography/Magnetoencephalography (EEG/MEG) source localization involves the estimation of neural activity inside the brain volume that underlies the EEG/MEG measures observed at the sensor array. In this paper, we consider a Bayesian finite spatial mixture model for source reconstruction and implement Ant Colony System (ACS) optimization coupled with Iterated Conditional Modes (ICM) for computing estimates of the neural source activity. Our approach is evaluated using simulation studies and a real data application in which we implement a nonparametric bootstrap for interval estimation. We demonstrate improved performance of the ACS-ICM algorithm as compared to existing methodology for the same spatiotemporal model.

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17

Tadel, François, Sylvain Baillet, John C. Mosher, Dimitrios Pantazis, and Richard M. Leahy. "Brainstorm: A User-Friendly Application for MEG/EEG Analysis." Computational Intelligence and Neuroscience 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/879716.

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Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).

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18

Henson, Richard N., Elias Mouchlianitis, and Karl J. Friston. "MEG and EEG data fusion: Simultaneous localisation of face-evoked responses." NeuroImage 47, no. 2 (August 2009): 581–89. http://dx.doi.org/10.1016/j.neuroimage.2009.04.063.

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19

Henson, RN, E. Mouchlianitis, and KJ Friston. "MEG and EEG data fusion: simultaneous localisation of face-evoked responses." NeuroImage 47 (July 2009): S167. http://dx.doi.org/10.1016/s1053-8119(09)71783-5.

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20

Dassios, George, and A. S. Fokas. "The definite non-uniqueness results for deterministic EEG and MEG data." Inverse Problems 29, no. 6 (May 9, 2013): 065012. http://dx.doi.org/10.1088/0266-5611/29/6/065012.

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21

Waldorp, Lourens J., Hilde M. Huizenga, Raoul P. P. P. Grasman, Koen B. E. Böcker, and Peter C. M. Molenaar. "Hypothesis testing in distributed source models for EEG and MEG data." Human Brain Mapping 27, no. 2 (2006): 114–28. http://dx.doi.org/10.1002/hbm.20170.

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22

Friston, K. J., K. M. Stephan, J. D. Heather, C. D. Frith, A. A. Ioannides, L. C. Liu, M. D. Rugg, et al. "A Multivariate Analysis of Evoked Responses in EEG and MEG Data." NeuroImage 3, no. 3 (June 1996): 167–74. http://dx.doi.org/10.1006/nimg.1996.0018.

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23

Itälinna, Veera, Hanna Kaltiainen, Nina Forss, Mia Liljeström, and Lauri Parkkonen. "Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data." PLOS Computational Biology 19, no. 11 (November 9, 2023): e1011613. http://dx.doi.org/10.1371/journal.pcbi.1011613.

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New biomarkers are urgently needed for many brain disorders; for example, the diagnosis of mild traumatic brain injury (mTBI) is challenging as the clinical symptoms are diverse and nonspecific. EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4–8 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG/EEG-based biomarkers.

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24

Hasasneh, Ahmad, Nikolas Kampel, Praveen Sripad, N. Jon Shah, and Jürgen Dammers. "Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data." Journal of Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1350692.

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We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.

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25

Boutros, Nash N., Klevest Gjini, Frank Wang, and Susan M. Bowyer. "Evoked Potentials Investigations of Deficit Versus Nondeficit Schizophrenia: EEG-MEG Preliminary Data." Clinical EEG and Neuroscience 50, no. 2 (September 3, 2018): 75–87. http://dx.doi.org/10.1177/1550059418797868.

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Heterogeneity of schizophrenia is a major obstacle toward understanding the disorder. One likely subtype is the deficit syndrome (DS) where patients suffer from predominantly negative symptoms. This study investigated the evoked responses and the evoked magnetic fields to identify the neurophysiological deviations associated with the DS. Ten subjects were recruited for each group (Control, DS, and Nondeficit schizophrenia [NDS]). Subjects underwent magnetoencephalography (MEG) and electroencephalography (EEG) testing while listening to an oddball paradigm to generate the P300 as well as a paired click paradigm to generate the mid-latency auditory-evoked responses (MLAER) in a sensory gating paradigm. MEG–coherence source imaging (CSI) during P300 task revealed a significantly higher average coherence value in DS than NDS subjects in the gamma band (30-80 Hz), when listening to standard stimuli but only NDS subjects had a higher average coherence level in the gamma band than controls when listening to the novel sounds. P50, N100, and P3a ERP amplitudes (EEG analysis) were significantly decreased in NDS compared with DS subjects. The data suggest that the deviations in the 2 patient groups are qualitatively different. Deviances in NDS patients suggest difficulty in both early (as in the gating paradigm), as well as later top-down processes (P300 paradigm). The main deviation in the DS group was an exaggerated responsiveness to ongoing irrelevant stimuli detected by EEG whereas NDS subjects had an exaggerated response to novelty.

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26

McClay, Wilbert. "A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases." Diseases 6, no. 4 (September 28, 2018): 89. http://dx.doi.org/10.3390/diseases6040089.

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In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI’s data analytics of a subject’s MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain–Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject’s response and users’ intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user’s brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.

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27

Schmitt, U., A. K. Louis, F. Darvas, H. Buchner, and M. Fuchs. "Numerical aspects of spatio-temporal current density reconstruction from EEG-/MEG-data." IEEE Transactions on Medical Imaging 20, no. 4 (April 2001): 314–24. http://dx.doi.org/10.1109/42.921480.

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28

Hipp, Joerg F., and Markus Siegel. "Accounting for Linear Transformations of EEG and MEG Data in Source Analysis." PLOS ONE 10, no. 4 (April 2, 2015): e0121048. http://dx.doi.org/10.1371/journal.pone.0121048.

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Leske, Sabine, and Sarang S. Dalal. "Reducing power line noise in EEG and MEG data via spectrum interpolation." NeuroImage 189 (April 2019): 763–76. http://dx.doi.org/10.1016/j.neuroimage.2019.01.026.

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30

Schwartz, D., D. Lemoine, E. Poiseau, and C. Barillot. "Registration of MEG/EEG data with 3D MRI: Methodology and precision issues." Brain Topography 9, no. 2 (December 1996): 101–16. http://dx.doi.org/10.1007/bf01200710.

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31

Ewald, Arne, and Guido Nolte. "Estimating true brain connectivity from EEG/MEG data invariant to coordinate transformations." Neuroscience Letters 500 (July 2011): e8. http://dx.doi.org/10.1016/j.neulet.2011.05.086.

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32

Pellegrino, Giovanni, Tanguy Hedrich, Rasheda Chowdhury, Jeffery A. Hall, Jean-Marc Lina, Francois Dubeau, Eliane Kobayashi, and Christophe Grova. "Source localization of the seizure onset zone from ictal EEG/MEG data." Human Brain Mapping 37, no. 7 (April 5, 2016): 2528–46. http://dx.doi.org/10.1002/hbm.23191.

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33

Verkhlyutov, V. M., E. O. Burlakov, K. G. Gurtovoy, and V. L. Vvedensky. "RECOGNITION OF ORAL SPEECH ACCORDING TO MEG DATA BY COVARIANCE FILTERS." Журнал высшей нервной деятельности им. И.П. Павлова 73, no. 6 (November 1, 2023): 800–808. http://dx.doi.org/10.31857/s0044467723060126.

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Speech recognition based on EEG and MEG data is the first step in the development of BCI and AI systems for their further use in inner speech decoding. Great advances in this direction have been made using ECoG and stereo-EEG. At the same time, there are few works on this topic on the analysis of data obtained by non-invasive methods of recording brain activity. Our approach is based on the evaluation of connections in the space of sensors with the identification of a pattern of MEG connectivity specific for a given segment of speech. We tested our method on 7 subjects. In all cases, our processing pipeline was quite reliable and worked either without recognition errors or with a small number of errors. After “training”, the algorithm is able to recognise a fragment of oral speech with a single presentation. For recognition, we used segments of the MEG recording 50–1200 ms from the beginning of the sound of the word. For high-quality recognition, a segment of at least 600 ms was required. Intervals longer than 1200 ms worsened the recognition quality. Bandpass filtering of the MEG showed that the quality of recognition is equally effective in the entire frequency range. Some decrease in the level of recognition is observed only in the range of 9–14 Hz.

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Neugebauer, Frank, Marios Antonakakis, Kanjana Unnwongse, Yaroslav Parpaley, Jörg Wellmer, Stefan Rampp, and Carsten H. Wolters. "Validating EEG, MEG and Combined MEG and EEG Beamforming for an Estimation of the Epileptogenic Zone in Focal Cortical Dysplasia." Brain Sciences 12, no. 1 (January 14, 2022): 114. http://dx.doi.org/10.3390/brainsci12010114.

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MEG and EEG source analysis is frequently used for the presurgical evaluation of pharmacoresistant epilepsy patients. The source localization of the epileptogenic zone depends, among other aspects, on the selected inverse and forward approaches and their respective parameter choices. In this validation study, we compare the standard dipole scanning method with two beamformer approaches for the inverse problem, and we investigate the influence of the covariance estimation method and the strength of regularization on the localization performance for EEG, MEG, and combined EEG and MEG. For forward modelling, we investigate the difference between calibrated six-compartment and standard three-compartment head modelling. In a retrospective study, two patients with focal epilepsy due to focal cortical dysplasia type IIb and seizure freedom following lesionectomy or radiofrequency-guided thermocoagulation (RFTC) used the distance of the localization of interictal epileptic spikes to the resection cavity resp. RFTC lesion as reference for good localization. We found that beamformer localization can be sensitive to the choice of the regularization parameter, which has to be individually optimized. Estimation of the covariance matrix with averaged spike data yielded more robust results across the modalities. MEG was the dominant modality and provided a good localization in one case, while it was EEG for the other. When combining the modalities, the good results of the dominant modality were mostly not spoiled by the weaker modality. For appropriate regularization parameter choices, the beamformer localized better than the standard dipole scan. Compared to the importance of an appropriate regularization, the sensitivity of the localization to the head modelling was smaller, due to similar skull conductivity modelling and the fixed source space without orientation constraint.

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Grova, Christophe, Maria Aiguabella, Rina Zelmann, Jean-Marc Lina, Jeffery A. Hall, and Eliane Kobayashi. "Intracranial EEG potentials estimated from MEG sources: A new approach to correlate MEG and iEEG data in epilepsy." Human Brain Mapping 37, no. 5 (March 2, 2016): 1661–83. http://dx.doi.org/10.1002/hbm.23127.

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36

Duman, Ali Nabi, and Ahmet E. Tatar. "Topological data analysis for revealing dynamic brain reconfiguration in MEG data." PeerJ 11 (July 20, 2023): e15721. http://dx.doi.org/10.7717/peerj.15721.

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In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.

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37

Hirayama, Jun-ichiro, Takeshi Ogawa, and Aapo Hyvärinen. "Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis." Neural Computation 27, no. 7 (July 2015): 1373–404. http://dx.doi.org/10.1162/neco_a_00747.

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Unsupervised analysis of the dynamics (nonstationarity) of functional brain connectivity during rest has recently received a lot of attention in the neuroimaging and neuroengineering communities. Most studies have used functional magnetic resonance imaging, but electroencephalography (EEG) and magnetoencephalography (MEG) also hold great promise for analyzing nonstationary functional connectivity with high temporal resolution. Previous EEG/MEG analyses divided the problem into two consecutive stages: the separation of neural sources and then the connectivity analysis of the separated sources. Such nonoptimal division into two stages may bias the result because of the different prior assumptions made about the data in the two stages. We propose a unified method for separating EEG/MEG sources and learning their functional connectivity (coactivation) patterns. We combine blind source separation (BSS) with unsupervised clustering of the activity levels of the sources in a single probabilistic model. A BSS is performed on the Hilbert transforms of band-limited EEG/MEG signals, and coactivation patterns are learned by a mixture model of source envelopes. Simulation studies show that the unified approach often outperforms conventional two-stage methods, indicating further the benefit of using Hilbert transforms to deal with oscillatory sources. Experiments on resting-state EEG data, acquired in conjunction with a cued motor imagery or nonimagery task, also show that the states (clusters) obtained by the proposed method often correlate better with physiologically meaningful quantities than those obtained by a two-stage method.

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Cui, Yuan, Shan Gao, and Junpeng Zhang. "ITERATIVE MUSIC FOR HIGHLY CORRELATED EEG/MEG SOURCE LOCALIZATION." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (April 2013): 1350019. http://dx.doi.org/10.4015/s1016237213500191.

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This study presented an iterative MUSIC (Multiple Signal Classification) for highly correlated EEG source localization. By suppressing the equivalent false source, the approximate true source location information was obtained. And then, by iteratively suppressing source found in the last iteration, eventually, both of the sources were identified. The method is designed to tackle highly correlated sources, for example, bilateral activations at primary auditory/auditory cortices, at which cases conventional MUSIC has difficulty. Compared with other similar methods, the presented one needs less computation load since it utilizes the minor difference between sources, as can be adequately explained by a theoretical model for correlated sources. Simulation and real data test confirmed its effectiveness.

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Leclercq, Yves, Jessica Schrouff, Quentin Noirhomme, Pierre Maquet, and Christophe Phillips. "fMRI Artefact Rejection and Sleep Scoring Toolbox." Computational Intelligence and Neuroscience 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/598206.

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We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FAST”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FAST tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. “FAST” is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. “FAST” is available for free, under the GNU-GPL.

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Peyk, Peter, Andrea De Cesarei, and Markus Junghöfer. "ElectroMagnetoEncephalography Software: Overview and Integration with Other EEG/MEG Toolboxes." Computational Intelligence and Neuroscience 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/861705.

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EMEGS (electromagnetic encephalography software) is a MATLAB toolbox designed to provide novice as well as expert users in the field of neuroscience with a variety of functions to perform analysis of EEG and MEG data. The software consists of a set of graphical interfaces devoted to preprocessing, analysis, and visualization of electromagnetic data. Moreover, it can be extended using a plug-in interface. Here, an overview of the capabilities of the toolbox is provided, together with a simple tutorial for both a standard ERP analysis and a time-frequency analysis. Latest features and future directions of the software development are presented in the final section.

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Bijma, Fetsje, Jan C. de Munck, Koen B. E. Böcker, Hilde M. Huizenga, and Rob M. Heethaar. "The coupled dipole model: an integrated model for multiple MEG/EEG data sets." NeuroImage 23, no. 3 (November 2004): 890–904. http://dx.doi.org/10.1016/j.neuroimage.2004.06.038.

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Moiseev, Alexander, and Robert Cropp. "Bayesian classification of index finger movements by analysis of MEG and EEG data." International Congress Series 1300 (June 2007): 349–52. http://dx.doi.org/10.1016/j.ics.2007.03.006.

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Lin, Sheng-Kai, Rong-Chin Lo, and Ren-Guey Lee. "MAGNETOENCEPHALOGRAPHY–ELECTROENCEPHALOGRAPHY CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM." Biomedical Engineering: Applications, Basis and Communications 32, no. 03 (June 2020): 2050024. http://dx.doi.org/10.4015/s1016237220500246.

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This study proposes an advanced co-registration method for an integrated high temporal resolution electroencephalography (EEG) and magnetoencephalography (MEG) data. The MEG has a higher accuracy for source localization techniques and spatial resolution by sensing magnetic fields generated by the entire brain using multichannel superconducting quantum interference devices, whereas EEG can record electrical activities from larger cortical surface to detect epilepsy. However, by integrating the two modality tools, we can accurately localize the epileptic activity compared to other non-invasive modalities. Integrating the two modality tools is challenging and important. This study proposes a new algorithm using an extended three-dimensional generalized Hough transform (3D GHT) to co-register the two modality data. The pre-process steps require the locations of EEG electrodes, MEG sensors, head-shape points of subjects and fiducial landmarks. The conventional GHT algorithm is a well-known method used for identifying or locating two 2D images. This study proposes a new co-registration method that extends the 2D GHT algorithm to a 3D GHT algorithm that can automatically co-register 3D image data. It is important to study the prospective brain source activity in bio-signal analysis. Furthermore, the study examines the registration accuracy evaluation by calculating the root mean square of the Euclidean distance of MEG–EEG co-registration data. Several experimental results are used to show that the proposed method for co-registering the two modality data is accurate and efficient. The results demonstrate that the proposed method is feasible, sufficiently automatic, and fast for investigating brain source images.

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Dalal, Sarang S., Johanna M. Zumer, Adrian G. Guggisberg, Michael Trumpis, Daniel D. E. Wong, Kensuke Sekihara, and Srikantan S. Nagarajan. "MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG." Computational Intelligence and Neuroscience 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/758973.

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NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.

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Edmonds, Benjamin D., William Welch, Yoshimi Sogawa, James Mountz, Anto Bagić, and Christina Patterson. "The Role of Magnetoencephalography and Single-Photon Emission Computed Tomography in Evaluation of Children With Drug-Resistant Epilepsy." Journal of Child Neurology 36, no. 8 (March 5, 2021): 673–79. http://dx.doi.org/10.1177/0883073821996558.

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Surgery holds the best outcomes for drug-resistant epilepsy in children, making localization of a seizure focus essential. However, there is limited research on the contribution of magnetoencephalography and single-photon emission computed tomography (SPECT) to the presurgical evaluation of lesional and nonlesional pediatric patients. This study proposed to evaluate the concordance of SPECT and magnetoencephalography (MEG) to scalp electroencephalography (EEG) to determine their effective contribution to the presurgical evaluation. On review, MEG and SPECT studies for 28 drug-resistant epilepsy cases were completed at Children’s Hospital of Pittsburgh from May 2012 to August 2018. Although not reaching statistical significance, MEG had increased lobar concordance with EEG compared with SPECT (68% vs 46%). MEG or SPECT results effectively provided localization data leading to 6 surgical evaluations and 3 resections with outcomes of Engel class I or II at 12 months. This study suggests MEG and SPECT provide valuable localizing information for presurgical epilepsy evaluation of children with drug-resistant epilepsy.

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Vecchiato, Giovanni, Laura Astolfi, Fabrizio De Vico Fallani, Jlenia Toppi, Fabio Aloise, Francesco Bez, Daming Wei, et al. "On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/643489.

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Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

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Rampp, Stefan, Martin Kaltenhäuser, Nadia Müller-Voggel, Arnd Doerfler, Burkhard S. Kasper, Hajo M. Hamer, Sebastian Brandner, and Michael Buchfelder. "MEG Node Degree for Focus Localization: Comparison with Invasive EEG." Biomedicines 11, no. 2 (February 2, 2023): 438. http://dx.doi.org/10.3390/biomedicines11020438.

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Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may add information to the workup of epilepsy surgery. Here, we investigate node degree (ND), a graph-theoretical parameter of functional connectivity, in relation to the seizure onset zone (SOZ) determined by invasive EEG (iEEG) in a consecutive series of 50 adult patients. Resting state data were subjected to whole brain, all-to-all connectivity analysis using the imaginary part of coherence. Graphs were described using parcellated ND. SOZ localization was investigated on a lobar and sublobar level. On a lobar level, all frequency bands except alpha showed significantly higher maximal ND (mND) values inside the SOZ compared to outside (ratios 1.11–1.20, alpha 1.02). Area-under-the-curve (AUC) was 0.67–0.78 for all expected alpha (0.44, ns). On a sublobar level, mND inside the SOZ was higher for all frequency bands (1.13–1.38, AUC 0.58–0.78) except gamma (1.02). MEG ND is significantly related to SOZ in delta, theta and beta bands. ND may provide new localization tools for presurgical evaluation of epilepsy surgery.

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Kang Cheng and Changhua Zou. "Theoretically modeling oscillations and waves (EEG and MEG signals) of the brain neuronal fluids and extracellular fluids, using plasma hydrodynamics." International Journal of Science and Research Archive 10, no. 2 (December 30, 2023): 1036–47. http://dx.doi.org/10.30574/ijsra.2023.10.2.1075.

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Introduction: Ionic oscillations and waves of the brain neurons are mostly analyzed and recorded by EEG (electroencephalography) and (or) MEG (magnetoencephalography). In principle EEG and MEG signals arise from the same neuronal sources. The traditional models of EEG and MEG do not involve natural excitations and attenuations, encodings (decodings), displacement currents of the brain neuronal electromagnetic signals, nor active pumps and passive channels of biological ions. Besides, we have not found any published research at an ionic level to theoretically describe the mechanisms how oscillations and waves of the brain neuronal fluids and extracellular fluids are excited, attenuated and maintained in the both natural and forced modes. Methods and Results: We introduce the plasma physics into brain theory; based on plasma hydrodynamic equations and published data of the brain or neuron sciences and molecular biology, at an ionic level, we model the mechanisms of the complete procedures of excitations, attenuations, propagations (oscillations and waves) of the brain neuronal fluids and extracellular fluids in the both natural and forced modes; our models include active pumps and passive channels of biological ions. Moreover, we also elucidate frequency and amplitude modulations (encodings), displacement currents, as well as effective values of the alternating electric current densities, electric and magnetic fields and voltages, based on the modeling results of the brain neuronal and extracellular plasma waves (oscillations). Conclusion: Our modeling results are qualitatively consistent with the published data of brain neuroscience as well as EEG and MEG.

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Ahlfors, Seppo P., and Maria Mody. "Overview of MEG." Organizational Research Methods 22, no. 1 (November 9, 2016): 95–115. http://dx.doi.org/10.1177/1094428116676344.

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Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.

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Haumann, Niels Trusbak, Lauri Parkkonen, Marina Kliuchko, Peter Vuust, and Elvira Brattico. "Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7489108.

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We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal—slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low—in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.

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