(ISSN 2573-4407)
OBM Neurobiology is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. By design, the scope of OBM Neurobiology is broad, so as to reflect the multidisciplinary nature of the field of Neurobiology that interfaces biology with the fundamental and clinical neurosciences. As such, OBM Neurobiology embraces rigorous multidisciplinary investigations into the form and function of neurons and glia that make up the nervous system, either individually or in ensemble, in health or disease. OBM Neurobiology welcomes original contributions that employ a combination of molecular, cellular, systems and behavioral approaches to report novel neuroanatomical, neuropharmacological, neurophysiological and neurobehavioral findings related to the following aspects of the nervous system: Signal Transduction and Neurotransmission; Neural Circuits and Systems Neurobiology; Nervous System Development and Aging; Neurobiology of Nervous System Diseases (e.g., Developmental Brain Disorders; Neurodegenerative Disorders).
OBM Neurobiology publishes a variety of article types (Original Research, Review, Communication, Opinion, Comment, Conference Report, Technical Note, Book Review, etc.). Although the OBM Neurobiology Editorial Board encourages authors to be succinct, there is no restriction on the length of the papers. Authors should present their results in as much detail as possible, as reviewers are encouraged to emphasize scientific rigor and reproducibility.
Publication Speed (median values for papers published in 2024): Submission to First Decision: 7.6 weeks; Submission to Acceptance: 13.6 weeks; Acceptance to Publication: 6 days (1-2 days of FREE language polishing included)
Special Issue
Explainable AI Methods for Applications in Brain–Computer Interface EEG Signals
Submission Deadline: July 15, 2026 (Open) Submit Now
Guest Editor
Hadi Seyedarabi, PhD
Professor of Electrical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Research Interests: Image segmentation; computer vision; neural networks; social psychology; emotional communication; medical signal processing; pattern recognition; AI in healthcare
Co-Editor
Mahsa Zeynali, PhD
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Research Interests: Machine learning and deep learning; brain-computer interface (BCI); biomedical signals and image processing; cognitive neuroscience; explainable AI
About This Topic
Brain-Computer Interfaces (BCIs), a technique that connects the human brain to a computational system, have led to remarkable advancements in medical applications, rehabilitation solutions, and brain-controlled devices. However, one of the major challenges of these systems is ensuring the transparency and explainability of their decision-making processes. Many machine learning models, particularly deep learning approaches used for analyzing electroencephalogram (EEG) signals, are often considered "black boxes," which diminishes trust among end-users, observers, and practitioners. Explainable AI (XAI) methods consist of techniques that aim to provide a better understanding of model behavior by explaining how a model makes predictions. Given the rapid transition from theory to practice and the crucial role of explainability in the acceptance and usability of BCIs in real-world applications, we invite researchers to contribute their findings in this area to help pave the way for more transparent and reliable brain-computer interfaces.
Keywords
Brain-computer interfaces (BCIs); explainable AI (XAI); electroencephalogram (EEG); machine learning; deep learning; brain signals
Manuscript Submission Information
Manuscripts should be submitted through the LIDSEN Submission System. Detailed information on manuscript preparation and submission is available in the Instructions for Authors. All submitted articles will be thoroughly refereed through a single-blind peer-review process and will be processed following the Editorial Process and Quality Control policy. Upon acceptance, the article will be immediately published in a regular issue of the journal and will be listed together on the special issue website, with a label that the article belongs to the Special Issue. LIDSEN distributes articles under the Creative Commons Attribution (CC BY 4.0) License in an open-access model. The authors own the copyright to the article, and the article can be free to access, distribute, and reuse provided that the original work is correctly cited.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). Research articles and review articles are highly invited. Authors are encouraged to send the tentative title and abstract of the planned paper to the Editorial Office (neurobiology@lidsen.com) for record. If you have any questions, please do not hesitate to contact the Editorial Office.
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2023 
CiteScore SJR SNIP 1.0 0.232 0.256 - Newsletter