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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (25 February 2020) | Viewed by 25516

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Special Issue Editor

Dr. Samu Taulu

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Guest Editor

1. Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, USA;
2. Department of Physics, University of Washington, Seattle, WA, USA.
Interests: magnetoencephalography; physics; biomagnetism; signal processing; source imaging; inverse models; electroencephalography

Special Issue Information

Dear Colleagues,

Magnetoencephalography (MEG) and electroencephalography (EEG) provide unprecedented means to perform non-invasive imaging of brain functions with a spatiotemporal resolution that enables a large variety of informative neuroscience findings. These findings help us better understand how the different functional brain areas operate, how they are connected, and how disruptions in this complicated system of interconnected areas can lead to neurological disorders. The ultimate goal of MEG/EEG studies is the reconstruction of the distribution of neural currents that is in accordance with the measured multi-channel signal distribution. In addition to the challenge posed by the non-unique nature of the MEG/EEG inverse problem, there are other complications that have prompted method developers to produce mathematical methods and algorithms ranging from general-purpose analysis tools to highly specific methods that aim at increasing robustness, e.g., by limiting the model complexity based on assumptions regarding the properties of the underlying current.

With the recent rapid developments in inverse methodology, connectivity models, and new MEG sensor technology that may revolutionize our ability to capture previously undetectable fine details of brain signals, a review of the most novel source imaging methods is timely. In this Special Issue on “Advances in EEG/MEG Source Imaging”, we would like to invite contributions demonstrating the most recent insights leading to the improved accuracy and robustness of source reconstruction based on multichannel MEG/EEG data.

Dr. Samu Taulu
Guest Editor

Manuscript Submission Information

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Keywords

Inverse model
Forward model
Volume conductor
Multichannel data
Brain areas
Connectivity
Source model

Published Papers (5 papers)

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Research

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14 pages, 1530 KiB

Open AccessArticle

The Temporal and Spatial Dynamics of Cortical Emotion Processing in Different Brain Frequencies as Assessed Using the Cluster-Based Permutation Test: An MEG Study

by Mina Kheirkhah, Philipp Baumbach, Lutz Leistritz, Stefan Brodoehl, Theresa Götz, Ralph Huonker, Otto W. Witte and Carsten M. Klingner

Brain Sci. 2020, 10(6), 352; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10060352 - 06 Jun 2020

Cited by 6 | Viewed by 3220

Abstract

The processing of emotions in the human brain is an extremely complex process that extends across a large number of brain areas and various temporal processing steps. In the case of magnetoencephalography (MEG) data, various frequency bands also contribute differently. Therefore, in most studies, the analysis of emotional processing has to be limited to specific sub-aspects. Here, we demonstrated that these problems can be overcome by using a nonparametric statistical test called the cluster-based permutation test (CBPT). To the best of our knowledge, our study is the first to apply the CBPT to MEG data of brain responses to emotional stimuli. For this purpose, different emotionally impacting (pleasant and unpleasant) and neutral pictures were presented to 17 healthy subjects. The CBPT was applied to the power spectra of five brain frequencies, comparing responses to emotional versus neutral stimuli over entire MEG channels and time intervals within 1500 ms post-stimulus. Our results showed significant clusters in different frequency bands, and agreed well with many previous emotion studies. However, the use of the CBPT allowed us to easily include large numbers of MEG channels, wide frequency, and long time-ranges in one study, which is a more reliable alternative to other studies that consider only specific sub-aspects. Full article

(This article belongs to the Special Issue Advances in EEG/ MEG Source Imaging )

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11 pages, 3393 KiB

Open AccessArticle

The Maximum Eigenvalue of the Brain Functional Network Adjacency Matrix: Meaning and Application in Mental Fatigue Evaluation

by Gang Li, Yonghua Jiang, Weidong Jiao, Wanxiu Xu, Shan Huang, Zhao Gao, Jianhua Zhang and Chengwu Wang

Brain Sci. 2020, 10(2), 92; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10020092 - 09 Feb 2020

Cited by 13 | Viewed by 2859

Abstract

The maximum eigenvalue of the adjacency matrix (AM) has been supposed to contain rich information about the corresponding network. An experimental study focused on revealing the meaning and application of the maximum eigenvalue is missing. To this end, AM was constructed using mutual information (MI) to determine the functional connectivity with electroencephalogram (EEG) data recorded with a mental fatigue model, and then was converted into both binary and weighted brain functional network (BFN) and corresponding random networks (RNs). Both maximum eigenvalue and corresponding network characters in BFNs and RNs were considered to explore the changes during the formation of mental fatigue. The results indicated that large maximum eigenvalue means more edges in the corresponding network, along with a high degree and a short characteristic path length both in weighted and binary BFNs. Interestingly, the maximum eigenvalue of AM was always a little larger than that of the corresponding random matrix (RM), and had an obvious linearity with the sum of the AM elements, indicating that the maximum eigenvalue can be able to distinguish the network structures which have the same mean degree. What is more, the maximum eigenvalue, which increased with the deepening of mental fatigue, can become a good indicator for mental fatigue estimation. Full article

(This article belongs to the Special Issue Advances in EEG/ MEG Source Imaging )

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20 pages, 4787 KiB

Open AccessArticle

Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source Localization Study

by Rupesh Kumar Chikara and Li-Wei Ko

Brain Sci. 2019, 9(9), 216; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9090216 - 27 Aug 2019

Cited by 18 | Viewed by 4300

Abstract

Auditory alarms are used to direct people’s attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments. Full article

(This article belongs to the Special Issue Advances in EEG/ MEG Source Imaging )

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16 pages, 2416 KiB

Open AccessArticle

Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

by Ömer Türk and Mehmet Siraç Özerdem

Brain Sci. 2019, 9(5), 115; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9050115 - 17 May 2019

Cited by 125 | Viewed by 9850

Abstract

The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%. Full article

(This article belongs to the Special Issue Advances in EEG/ MEG Source Imaging )

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Review

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27 pages, 1526 KiB

Open AccessReview

Magnetic Source Imaging and Infant MEG: Current Trends and Technical Advances

by Chieh Kao and Yang Zhang

Brain Sci. 2019, 9(8), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9080181 - 27 Jul 2019

Cited by 6 | Viewed by 4637

Abstract

Magnetoencephalography (MEG) is known for its temporal precision and good spatial resolution in cognitive brain research. Nonetheless, it is still rarely used in developmental research, and its role in developmental cognitive neuroscience is not adequately addressed. The current review focuses on the source analysis of MEG measurement and its potential to answer critical questions on neural activation origins and patterns underlying infants’ early cognitive experience. The advantages of MEG source localization are discussed in comparison with functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), two leading imaging tools for studying cognition across age. Challenges of the current MEG experimental protocols are highlighted, including measurement and data processing, which could potentially be resolved by developing and improving both software and hardware. A selection of infant MEG research in auditory, speech, vision, motor, sleep, cross-modality, and clinical application is then summarized and discussed with a focus on the source localization analyses. Based on the literature review and the advancements of the infant MEG systems and source analysis software, typical practices of infant MEG data collection and analysis are summarized as the basis for future developmental cognitive research. Full article

(This article belongs to the Special Issue Advances in EEG/ MEG Source Imaging )

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