Machine Learning and Signal Processing for EEG, ECG, EDA, and Other Biosignals
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 487
Special Issue Editors
Interests: computational neuroscience; artificial intelligence in health
Interests: natural language processing; information retrieval; question answering; computer musicology
Interests: brain–computer interface; human–robot interaction; signal processing; rehabilitation robotics; assistive technology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Understanding brain activity is a challenge due to its high structural and functional complexity, as well as its high inter- and intra-subject variability. One of the most promising methods of observing and studying brain activity is through the spatio-temporal domain, using machine learning (ML) techniques applied to biosignals such as electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), etc.
The increasing utilization of brain–computer interfaces (BCI) for clinical applications requires an the processing of EEG, ECG, EDA signals to be significantly improved and for suitable ML and deep learning (DL) techniques to be adopted for practical applications. The processing and analysis of biosignals can be appropriately exploited to detect anomalies in pathological states and improve the early diagnosis of brain diseases. Signal processing and ML techniques applied to EEG, ECG, EDA, and other biosignals related to brain activity address problems such as noise, artifacts, volume conduction, brain connectivity, limited spatial resolution and high temporal resolution.
This Special Issue aims to collate articles that provide innovative contributions to the field of biosignal processing and present recent research on brain activity detection and analysis, as well as the application of artificial intelligence to EEG, ECG, and EDA data including, but not limited to, the following: feature-based ML approaches, artificial neural network architectures, statistical approaches in modeling, applications of graph theory, clinical diagnostics, emotion recognition, attention recognition, brain activity classification, brain–computer interfaces (BCI), artifact removal, and brain connectivity analysis.
Finally, we would like to thank Ms. Cristina Del Prete for her help in the creation of this Special Issue.
Dr. Vito De Feo
Dr. Richard Sutcliffe
Dr. Anirban Chowdhury
Dr. Rab Nawaz
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. Algorithms 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 1600 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
- machine learning
- signal processing
- EEG
- artificial neural network architectures
- clinical diagnostics
- emotion recognition
- brain–computer interfaces
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