Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Recent Advances in Neurocognitive Analysis Based on EEG Signal Processing
share announcement

Recent Advances in Neurocognitive Analysis Based on EEG Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 15602

Special Issue Editors

Dr. Alejandro L. Borja

E-Mail Website
Guest Editor
Departamento de Ingeniería Eléctrica, Electrónica, Automática y Comunicaciones, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: neurocognitive deficits; brain stimulation; electroencephalography; brain computer interface; machine learning; deep learning; neural networks; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jorge J. Ricarte

E-Mail Website
Guest Editor
Departamento de Psicología. Instituto de Investigación en Discapacidades Neurológicas (IDINE), University of Castilla-La Mancha, 02071 Albacete, Spain
Interests: neurocognitive deficits; autobiographical recall; aging; schizophrenia; depression; neurofeedback; psychopathology; brain stimulation

Special Issue Information

Dear Colleagues,

The study of data derived from electroencephalography (EEG) can yield exhaustive information related to the physiological, functional, and pathological status of the brain. As a result, this information can be extremely useful for the identification of brain rhythms. The latest developments in signal processing and machine learning applied to EEG analysis have provided notable advancements in basic and clinical neuroscience. Hence, the combination of both, complete brain monitoring together with novel and improved signal processing, makes noninvasive EEG analysis a promising clinical tool for the diagnosis and treatment of brain disorders and/or impairments.

This Special Issue invites original research papers that report on recent advancements in EEG signal processing for neurocognitive analysis and its applications. Prospective authors are invited to submit high-quality contributions and reviews. Potential topics include, but are not limited to the following:

  • EEG signal processing
  • EEG Data analytics
  • BCI applications
  • Machine learning
  • Neural networks
  • Neural signal processing techniques (EEG, MEG, MRI/fMRI, PET, fNIRS)
  • Neurofeedback
  • Neurostimulation

Dr. Alejandro L. Borja
Dr. Jorge J. Ricarte
Guest Editors

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 5153 KiB  
Article
Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach
by Onofre R. Contreras-Jordán, Roberto Sánchez-Reolid, Álvaro Infantes-Paniagua, Antonio Fernández-Caballero and Francisco Tomás González-Fernández
Electronics 2022, 11(5), 770; https://doi.org/10.3390/electronics11050770 - 02 Mar 2022
Cited by 3 | Viewed by 2951
Abstract
Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to [...] Read more.
Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way. Full article
(This article belongs to the Special Issue Recent Advances in Neurocognitive Analysis Based on EEG Signal Processing)
►▼ Show Figures

Figure 1

14 pages, 3862 KiB  
Article
High-Precise Bipolar Disorder Detection by Using Radial Basis Functions Based Neural Network
by Miguel Ángel Luján, Ana M. Torres, Alejandro L. Borja, José L. Santos and Jorge Mateo Sotos
Electronics 2022, 11(3), 343; https://doi.org/10.3390/electronics11030343 - 23 Jan 2022
Cited by 5 | Viewed by 3141
Abstract
Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in [...] Read more.
Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in the scientific community. In this work, electroencephalogram records and patient data from the Hospital Virgen de la Luz in Cuenca (Spain) were processed for a correct classification of bipolar disorders. This work implemented an innovative radial basis function-based neural network employing a fuzzy means algorithm. The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. The proposed algorithm achieved the best performance compared with other machine learning techniques such as Bayesian linear discriminant analysis, Gaussian naive Bayes, decision trees, K-nearest neighbour, or support vector machine, showing a very high accuracy close to 97%. Therefore, the neural network technique presented could be used as a new tool for the diagnosis of bipolar disorder, considering the possibility of integrating this method into medical software. Full article
(This article belongs to the Special Issue Recent Advances in Neurocognitive Analysis Based on EEG Signal Processing)
►▼ Show Figures

Figure 1

18 pages, 774 KiB  
Article
Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis
by Serena Dattola, Nadia Mammone, Francesco Carlo Morabito, Domenico Rosaci, Giuseppe Maria Luigi Sarné and Fabio La Foresta
Electronics 2021, 10(12), 1440; https://doi.org/10.3390/electronics10121440 - 15 Jun 2021
Cited by 7 | Viewed by 1884
Abstract
Alzheimer’s Disease (AD) is an incurable neurodegenerative disorder which mainly affects older adults. An early diagnosis is essential because medical treatments can slow down the progression of the disease only if provided during the first stage, called Mild Cognitive Impairment (MCI). Starting from [...] Read more.
Alzheimer’s Disease (AD) is an incurable neurodegenerative disorder which mainly affects older adults. An early diagnosis is essential because medical treatments can slow down the progression of the disease only if provided during the first stage, called Mild Cognitive Impairment (MCI). Starting from the study of electroencephalografic signals, brain functional connectivity analyses can be performed with the support of the graph theory. In particular, the purpose of this work is to verify the performances of three indexes, typically adopted to evaluate the graph robustness, in order to estimate the functional connectivity for three groups of subjects: healthy controls and people affected by dementia at two different stages (MCI and AD). The results obtained by the Connection Density Index, the Randić Index, and a normalized version of the Kirchhoff Index revealed a higher robustness in the brain networks of healthy people, followed by MCI and, finally, by AD patients, consistent with the hallmarks of Alzheimer’s disease. The statistical analysis showed that there is a significant difference between controls and AD for all three indexes. Finally, all three indexes were compared, revealing that the the Randić Index outperformed the other two indexes. These preliminary outcomes will be exploited to address further in-depth and time-expensive analyses for improving the diagnosis of Alzheimer’s disease. Full article
(This article belongs to the Special Issue Recent Advances in Neurocognitive Analysis Based on EEG Signal Processing)
►▼ Show Figures

Figure 1

Review

Jump to: Research

19 pages, 2588 KiB  
Review
A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia
by Miguel Ángel Luján, María Verónica Jimeno, Jorge Mateo Sotos, Jorge Javier Ricarte and Alejandro L. Borja
Electronics 2021, 10(23), 3037; https://doi.org/10.3390/electronics10233037 - 05 Dec 2021
Cited by 32 | Viewed by 6117
Abstract
In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity [...] Read more.
In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined. Full article
(This article belongs to the Special Issue Recent Advances in Neurocognitive Analysis Based on EEG Signal Processing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop