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Advancements in EEG and Biosignal Sensing Technologies
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Advancements in EEG and Biosignal Sensing Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2417

Special Issue Editors

Dr. Sergio Iglesias-Parro

E-Mail Website
Guest Editor
Psychology Department, University of Jaén, 23071 Jaén, Spain
Interests: EEG signal analysis; complexity; consciousness
Dr. Antonio José Ibáñez-Molina

E-Mail Website
Guest Editor
Psychology Department, University of Jaén, 23071 Jaén, Spain
Interests: neural bases of consciousness; complexity analysis on psychophysiological time series; computational whole brain models

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the emerging field of EEG biosignal sensing technologies. Over the years, EEG has evolved into a powerful tool for understanding brain function and diagnosing various neurological disorders. Recent advancements in sensor technology, signal processing, and machine learning algorithms have paved the way for exciting developments in EEG biosignal sensing technologies, enabling novel applications and enhancing our understanding of the brain.

The Special Issue aims to explore the latest research and innovations in EEG biosignal sensing technologies and their applications across diverse domains. It aims to collate contributions from researchers, engineers, and scientists working in the field of neurotechnology and neuroscience. The objective is to provide a comprehensive overview of the state-of-the-art advancements, challenges, and future directions in EEG biosignal sensing.

The topics addressed in this Special Issue encompass an extensive range of areas, including, but not limited to, the following:

  • Sensor technology advancements: Novel electrode designs, wearable sensors, and wireless EEG systems.
  • Signal processing techniques: Advanced algorithms for noise reduction, artifact removal, feature extraction, and analysis of EEG signals.
  • Brain–computer interfaces (BCIs): Development of BCIs using EEG biosignal sensing for communication, control, and neurorehabilitation.
  • Machine learning approaches: Applications of machine learning algorithms for EEG signal classification, pattern recognition, and prediction.
  • Neurological disorder diagnosis and treatment: Utilizing EEG biosignal sensing technologies for early detection, monitoring, and treatment evaluation of neurological conditions such as epilepsy, Alzheimer's disease, and attention deficit hyperactivity disorder (ADHD).
  • Cognitive neuroscience and brain function: Investigating brain dynamics, cognition, and mental states using EEG biosignal sensing.
  • Neurofeedback and brain training: Techniques and applications for real-time feedback and training of brain activity.

This Special Issue aims to foster collaboration and an exchange of knowledge among researchers and practitioners in the field of EEG biosignal sensing technologies. It is anticipated that the contributions will elucidate the current state of the field, highlight innovative approaches, and inspire future research directions in order to unlock the full potential of EEG in understanding the complexities of the human brain.

Dr. Sergio Iglesias-Parro
Dr. Antonio José Ibáñez-Molina
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. Sensors 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 2600 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
  • biosignal sensing
  • neurotechnology
  • brain–computer interface
  • signal processing
  • machine learning
  • neurological disorders
  • brain function, sensor technology
  • neural activity
  • cognitive neuroscience

Published Papers (2 papers)

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Research

18 pages, 4450 KiB  
Article
Parallel Ictal-Net, a Parallel CNN Architecture with Efficient Channel Attention for Seizure Detection
by Gerardo Hernández-Nava, Sebastián Salazar-Colores, Eduardo Cabal-Yepez and Juan-Manuel Ramos-Arreguín
Sensors 2024, 24(3), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/s24030716 - 23 Jan 2024
Viewed by 803
Abstract
Around 70 million people worldwide are affected by epilepsy, a neurological disorder characterized by non-induced seizures that occur at irregular and unpredictable intervals. During an epileptic seizure, transient symptoms emerge as a result of extreme abnormal neural activity. Epilepsy imposes limitations on individuals [...] Read more.
Around 70 million people worldwide are affected by epilepsy, a neurological disorder characterized by non-induced seizures that occur at irregular and unpredictable intervals. During an epileptic seizure, transient symptoms emerge as a result of extreme abnormal neural activity. Epilepsy imposes limitations on individuals and has a significant impact on the lives of their families. Therefore, the development of reliable diagnostic tools for the early detection of this condition is considered beneficial to alleviate the social and emotional distress experienced by patients. While the Bonn University dataset contains five collections of EEG data, not many studies specifically focus on subsets D and E. These subsets correspond to EEG recordings from the epileptogenic zone during ictal and interictal events. In this work, the parallel ictal-net (PIN) neural network architecture is introduced, which utilizes scalograms obtained through a continuous wavelet transform to achieve the high-accuracy classification of EEG signals into ictal or interictal states. The results obtained demonstrate the effectiveness of the proposed PIN model in distinguishing between ictal and interictal events with a high degree of confidence. This is validated by the computing accuracy, precision, recall, and F1 scores, all of which consistently achieve around 99% confidence, surpassing previous approaches in the related literature. Full article
(This article belongs to the Special Issue Advancements in EEG and Biosignal Sensing Technologies)
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14 pages, 2762 KiB  
Article
Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach
by Sergio Iglesias-Parro, María F. Soriano, Antonio J. Ibáñez-Molina, Ana V. Pérez-Matres and Juan Ruiz de Miras
Sensors 2023, 23(21), 8722; https://0-doi-org.brum.beds.ac.uk/10.3390/s23218722 - 25 Oct 2023
Viewed by 1167
Abstract
Schizophrenia (SZ) is a complex disorder characterized by a range of symptoms and behaviors that have significant consequences for individuals, families, and society in general. Electroencephalography (EEG) is a valuable tool for understanding the neural dynamics and functional abnormalities associated with schizophrenia. Research [...] Read more.
Schizophrenia (SZ) is a complex disorder characterized by a range of symptoms and behaviors that have significant consequences for individuals, families, and society in general. Electroencephalography (EEG) is a valuable tool for understanding the neural dynamics and functional abnormalities associated with schizophrenia. Research studies utilizing EEG have identified specific patterns of brain activity in individuals diagnosed with schizophrenia that may reflect disturbances in neural synchronization and information processing in cortical circuits. Considering the temporal dynamics of functional connectivity provides a more comprehensive understanding of brain networks’ organization and how they change during different cognitive states. This temporal perspective would enhance our understanding of the underlying mechanisms of schizophrenia. In the present study, we will use measures based on graph theory to obtain dynamic and static indicators in order to evaluate differences in the functional connectivity of individuals diagnosed with SZ and healthy controls using an ecologically valid task. At the static level, patients showed alterations in their ability to segregate information, particularly in the default mode network (DMN). As for dynamic measures, patients showed reduced values in most metrics (segregation, integration, centrality, and resilience), reflecting a reduced number of dynamic states of brain networks. Our results show the utility of combining static and dynamic indicators of functional connectivity from EEG sensors. Full article
(This article belongs to the Special Issue Advancements in EEG and Biosignal Sensing Technologies)
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