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Algorithms
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Machine Learning and Signal Processing for EEG, ECG, EDA, and...
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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: 28 February 2026 | Viewed by 1767

Special Issue Editors

Dr. Vito De Feo

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: computational neuroscience; artificial intelligence in health
Dr. Richard Sutcliffe

E-Mail Website
Co-Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: natural language processing; information retrieval; question answering; computer musicology
Dr. Anirban Chowdhury

E-Mail Website
Co-Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: brain–computer interface; human–robot interaction; signal processing; rehabilitation robotics; assistive technology
Special Issues, Collections and Topics in MDPI journals
Dr. Rab Nawaz

E-Mail Website
Co-Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: brain–computer interface; biomedical signal processing; neurofeedback; electroencephalography

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 1800 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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Published Papers (2 papers)

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Research

22 pages, 1861 KB  
Article
Impact of Temporal Window Shift on EEG-Based Machine Learning Models for Cognitive Fatigue Detection
by Agnieszka Wosiak, Michał Sumiński and Katarzyna Żykwińska
Algorithms 2025, 18(10), 629; https://doi.org/10.3390/a18100629 - 5 Oct 2025
Viewed by 286
Abstract
In our study, we examine how the temporal window shift—the step between consecutive analysis windows—affects EEG-based cognitive fatigue detection while keeping the window length fixed. Using a reference workload dataset and a pipeline that includes preprocessing and feature extraction, we vary the shift [...] Read more.
In our study, we examine how the temporal window shift—the step between consecutive analysis windows—affects EEG-based cognitive fatigue detection while keeping the window length fixed. Using a reference workload dataset and a pipeline that includes preprocessing and feature extraction, we vary the shift to control segment overlap and, consequently, the number and independence of training samples. We evaluate six machine-learning models (decision tree, random forest, SVM, kNN, MLP, and a transformer). Across the models, smaller shifts generally increase accuracy and F1 score, consistent with the larger sample count; however, they also reduce sample independence and can inflate performance if evaluation splits are not sufficiently stringent. Class-wise analyses reveal persistent confusion for the moderate-fatigue class, the severity of which depends on the chosen shift. We discuss the methodological trade-offs, provide practical recommendations for choosing and reporting shift parameters, and argue that temporal segmentation decisions should be treated as first-class design choices in EEG classification. Our findings highlight the need for transparent reporting of window length, shift/overlap, and subject-wise evaluation protocols to ensure reliable and reproducible results in cognitive fatigue detection. Our conclusions pertain to subject-wise generalization on the STEW dataset; cross-dataset validation is an important next step. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for EEG, ECG, EDA, and Other Biosignals)
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19 pages, 1484 KB  
Article
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
by Niklas Grieger, Siamak Mehrkanoon and Stephan Bialonski
Algorithms 2025, 18(9), 580; https://doi.org/10.3390/a18090580 - 13 Sep 2025
Viewed by 475
Abstract
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on [...] Read more.
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed “frequency pretraining” to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for EEG, ECG, EDA, and Other Biosignals)
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