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Brain Sciences
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Brain Connectivity Analysis from EEG Signals
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Brain Connectivity Analysis from EEG Signals

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

Deadline for manuscript submissions: closed (20 November 2020) | Viewed by 7414

Special Issue Editor

Dr. Carlos Guerrero-Mosquera

E-Mail Website
Guest Editor
GTM group, Carrer de Sant Joan de la Salle 42, 08022, Ramon Llull University, Barcelona, Spain
Interests: Human brain connectivity; Functional connectivity; EEG; Intracranial EEG; Phase synchronization; Effective connectivity; Nonlinear synchronization; Electrophysiological connectivity; Electrophysiological connectome; Information based techniques; Coherence

Special Issue Information

Dear Colleagues,

A central paradigm in modern neuroscience is that even simple cognitive processes require anatomical and functional connections working together as a network and that these brain regions are organized in a way such that information processing is near-optimal. Now, algorithms and computational tools aim to identify and assess these networks and also to provide important findings in different EEG applications.

Brain connectivity thus consists in to analyse the spatially distributed but functionally connected regions that process brain information, and this rests upon three different but related forms of connectivity: Anatomical connectivity (AC), Functional connectivity (FC) and, Effective connectivity (EC).

This special thematic issue of Brain Sciences aims to assemble new theoretical approaches and computational solutions in Brain Connectivity Analysis from EEG signal. We invite papers for a special issue: “Brain connectivity analysis from EEG signals: new techniques and computational applications” in Brain Sciences Journal. This special issue welcomes contributions that engage with care in various ways and from a range of EEG applications. We welcome papers that computationally, methodologically and theoretically approach the growing importance of care for Brain Connectivity analysis.

Dr. Carlos Guerrero-Mosquera
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • EEG
  • Brain connectivity
  • Network
  • Functionally connected regions
  • Modern neuroscience

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Published Papers (2 papers)

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Research

26 pages, 6082 KiB  
Article
Modifications in the Topological Structure of EEG Functional Connectivity Networks during Listening Tonal and Atonal Concert Music in Musicians and Non-Musicians
by Almudena González, Manuel Santapau, Antoni Gamundí, Ernesto Pereda and Julián J. González
Brain Sci. 2021, 11(2), 159; https://doi.org/10.3390/brainsci11020159 - 26 Jan 2021
Cited by 3 | Viewed by 3193
Abstract
The present work aims to demonstrate the hypothesis that atonal music modifies the topological structure of electroencephalographic (EEG) connectivity networks in relation to tonal music. To this, EEG monopolar records were taken in musicians and non-musicians while listening to tonal, atonal, and pink [...] Read more.
The present work aims to demonstrate the hypothesis that atonal music modifies the topological structure of electroencephalographic (EEG) connectivity networks in relation to tonal music. To this, EEG monopolar records were taken in musicians and non-musicians while listening to tonal, atonal, and pink noise sound excerpts. EEG functional connectivities (FC) among channels assessed by a phase synchronization index previously thresholded using surrogate data test were computed. Sound effects, on the topological structure of graph-based networks assembled with the EEG-FCs at different frequency-bands, were analyzed throughout graph metric and network-based statistic (NBS). Local and global efficiency normalized (vs. random-network) measurements (NLE|NGE) assessing network information exchanges were able to discriminate both music styles irrespective of groups and frequency-bands. During tonal audition, NLE and NGE values in the beta-band network get close to that of a small-world network, while during atonal and even more during noise its structure moved away from small-world. These effects were attributed to the different timbre characteristics (sounds spectral centroid and entropy) and different musical structure. Results from networks topographic maps for strength and NLE of the nodes, and for FC subnets obtained from the NBS, allowed discriminating the musical styles and verifying the different strength, NLE, and FC of musicians compared to non-musicians. Full article
(This article belongs to the Special Issue Brain Connectivity Analysis from EEG Signals)
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15 pages, 2904 KiB  
Article
Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity
by Inna Feklicheva, Ilya Zakharov, Nadezda Chipeeva, Ekaterina Maslennikova, Svetlana Korobova, Timofey Adamovich, Victoria Ismatullina and Sergey Malykh
Brain Sci. 2021, 11(1), 94; https://doi.org/10.3390/brainsci11010094 - 13 Jan 2021
Cited by 1 | Viewed by 3089
Abstract
The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. [...] Read more.
The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance. Full article
(This article belongs to the Special Issue Brain Connectivity Analysis from EEG Signals)
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