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Integrating EEG and Multimodal Biomedical Sensors Data Analysis with Machine Learning

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

Deadline for manuscript submissions: 25 June 2025 | Viewed by 3687

Special Issue Editors

Dr. Xiaosu (Frank) Hu

E-Mail Website
Guest Editor
Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan, Ann Arbor, MI 48109, USA
Interests: brain imaging techniques (e.g., fNIRS, EEG); cognitive neuroscience study; imaging assisted clinical study; biomedical signal processing; machine/deep learning; large language model
Special Issues, Collections and Topics in MDPI journals
Dr. Hendrik Santosa

E-Mail Website
Guest Editor
Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
Interests: functional near-infrared spectroscopy (fNIRS) data processing; statistical analysis for fNIRS signal; multi-modal neuroimaging; brain-computer interface (BCI) applications
Special Issues, Collections and Topics in MDPI journals
Dr. Dalin Yang

E-Mail Website
Guest Editor
Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
Interests: autism spectrum disorders; medical imaging; fMRI; radiology; deep learning; machine learning; cognitive neuroscience; Python (programming Language); MATLAB; congenital heart disease; computer science; data analysis; neuroscience; medical research; neuroimaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a Special Issue in MDPI Sensors that is dedicated to the exploration of cutting-edge research and advancements in integrated EEG data analysis with machine learning and multimodal biomedical signal fusion. This Special Issue will bring together leading scholars, researchers, and experts from around the world to contribute their original and innovative work in this field. We invite submissions that address pivotal issues, share new insights, and offer fresh perspectives within the realms of EEG data analysis (with state-of-the art machine/deep learning techniques), the joint analysis of EEG and other neuroimaging modalities, or biomedical sensor signals. This Special Issue is an exceptional opportunity for researchers to showcase their work and engage in a scholarly exchange that can shape the future of this field. We look forward to receiving your contributions and promise a rigorous peer review process to ensure the highest quality of the published articles.

Dr. Xiaosu (Frank) Hu
Dr. Hendrik Santosa
Dr. Dalin Yang
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
  • machine learning
  • biomedical sensors
  • multimodal neuroimaging

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Published Papers (2 papers)

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Research

22 pages, 9234 KiB  
Article
Modeling and Parameter Analysis of Basic Single Channel Neuron Mass Model for SSVEP
by Depeng Gao, Yujuan Wang, Peirong Fu, Jianlin Qiu and Hongqi Li
Sensors 2025, 25(6), 1706; https://doi.org/10.3390/s25061706 - 10 Mar 2025
Viewed by 491
Abstract
While steady-state visual evoked potentials (SSVEPs) are widely used in brain–computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient [...] Read more.
While steady-state visual evoked potentials (SSVEPs) are widely used in brain–computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory–inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain–computer interfaces. Full article
(This article belongs to the Special Issue Integrating EEG and Multimodal Biomedical Sensors Data Analysis with Machine Learning)
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27 pages, 1835 KiB  
Article
Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer Interface System
by Md. Humaun Kabir, Nadim Ibne Akhtar, Nishat Tasnim, Abu Saleh Musa Miah, Hyoun-Sup Lee, Si-Woong Jang and Jungpil Shin
Sensors 2024, 24(15), 4989; https://doi.org/10.3390/s24154989 - 1 Aug 2024
Cited by 2 | Viewed by 2495
Abstract
The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many [...] Read more.
The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model’s strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods. Full article
(This article belongs to the Special Issue Integrating EEG and Multimodal Biomedical Sensors Data Analysis with Machine Learning)
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