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Biosensors
Special Issues
Applications of AI in Non-Invasive Biosensing Technologies
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Applications of AI in Non-Invasive Biosensing Technologies

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1204

Special Issue Editor

Dr. Xin Zhang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Tianjin Normal University, Tianjin 300387, China
Interests: EEG emotion recognition; EEG motor imagery; feature extraction; deep neural network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the transformative synergy between artificial intelligence (AI) and non-invasive biosensing, addressing the critical need for intelligent, real-time monitoring of physiological and neurocognitive states. Beyond traditional health monitoring, AI-powered analysis of Electroencephalography (EEG), functional Near-Infrared Spectroscopy (fNIRS), Electrocardiography (ECG), and Electromyography (EMG) signals now enables emotion recognition, motor imagery decoding, and brain–computer interfaces through sophisticated interpretation of complex biosignals. Recent advances in wearable biosensors generate high-dimensional data streams that demand advanced computational approaches for artifact removal, feature extraction, and predictive modeling in naturalistic settings.

We invite researchers to submit original research articles and reviews that address, but are not limited to, the following topics:

  1. AI-driven signal processing and data fusion from neurophysiological biosensors;
  2. Deep learning for decoding motor imagery signals of non-invasive biosensors;
  3. Deep learning for classifying emotion signals of non-invasive biosensors;
  4. Transfer learning for non-invasive biosensing brain–computer interface development;
  5. Explainable AI models for neurocognitive state interpretation;
  6. Transfer learning addressing subject variability in biosignal patterns;
  7. Non-invasive biosensing applications spanning clinical neurology, affective computing, and human–computer interaction.

Suitable submissions include original research on AI-enabled neurophysiological sensing systems, algorithm validation with open datasets, comprehensive reviews of AI applications in specific modalities (EEG/fNIRS/ECG/EMG), and translational studies demonstrating real-world efficacy. We particularly encourage work on non-invasive biosensing signal decoding, benchmark dataset creation, cross-subject solutions, cross-modal learning strategies, and robust model deployment in unconstrained environments.

You may choose our Joint Special Issue in Sensors.

Dr. Xin Zhang
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Biosensors 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 2200 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

  • deep learning
  • artificial intelligence
  • EEG
  • fNIRS
  • brain–computer interface
  • ECG
  • EMG
  • non-invasive biosensing

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Published Papers (2 papers)

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24 pages, 2535 KB  
Article
A Two-Stage EEG Microstate Fusion Framework for Dementia Screening and Alzheimer’s Disease/Frontotemporal Dementia Differentiation
by Lei Jiang, Yingna Chen, Yan He, Jiarui Liang, Xuan Zhao and Xiuyan Guo
Biosensors 2026, 16(5), 258; https://doi.org/10.3390/bios16050258 - 1 May 2026
Abstract
Differentiating Alzheimer’s disease (AD) from frontotemporal dementia (FTD) using resting-state electroencephalography (EEG) remains clinically challenging because of their overlapping electrophysiological characteristics. Although EEG suits large-scale dementia screening, current method often overestimates performance because of epoch-level data leakage and multiclass feature competition in unified [...] Read more.
Differentiating Alzheimer’s disease (AD) from frontotemporal dementia (FTD) using resting-state electroencephalography (EEG) remains clinically challenging because of their overlapping electrophysiological characteristics. Although EEG suits large-scale dementia screening, current method often overestimates performance because of epoch-level data leakage and multiclass feature competition in unified models. We propose a task-decoupled, two-stage hierarchical deep learning framework utilizing multiband EEG microstate dynamics. Continuous microstate sequences, modeled via Hungarian matching to preserve fine-grained temporal information, are processed using a normalizer-free 1D convolutional neural network (1D-CNN-NFNet) integrated with multi-head attention. By decoupling the workflow, Stage 1 performs generalized dementia screening using alpha and delta microstates, achieving an area under the curve (AUC) of 0.851. Stage 2 disentangles AD from FTD using delta and theta dynamics, yielding an AD-locking specificity of 86.1%. Evaluated under a strict subject-level leave-one-subject-out (LOSO) cross-validation protocol, the two-stage framework achieved 63.9% balanced accuracy, outperforming the single-stage baseline (55.4%) with a negligible inference latency of 0.733 ms. Furthermore, attention-based interpretability analysis links frequency-specific microstate alterations to underlying cortical disconnection syndromes. These results demonstrate that the framework provides a reproducible and interpretable auxiliary reference for dementia screening and subtyping in clinical neurology. Full article
(This article belongs to the Special Issue Applications of AI in Non-Invasive Biosensing Technologies)
19 pages, 2509 KB  
Article
Emotion Recognition Using Multi-View EEG-fNIRS and Cross-Attention Feature Fusion
by Ni Yan, Guijun Chen and Xueying Zhang
Biosensors 2026, 16(3), 145; https://doi.org/10.3390/bios16030145 - 2 Mar 2026
Viewed by 773
Abstract
To improve the accuracy of emotion recognition, this paper proposes a multi-view EEG-fNIRS and cross-attention fusion module named FGCN-TCNN-CAF, which employs a differentiated modeling strategy for the frequency, spatial, and temporal features of EEG-fNIRS signals. First, frequency-domain and time-domain features are extracted from [...] Read more.
To improve the accuracy of emotion recognition, this paper proposes a multi-view EEG-fNIRS and cross-attention fusion module named FGCN-TCNN-CAF, which employs a differentiated modeling strategy for the frequency, spatial, and temporal features of EEG-fNIRS signals. First, frequency-domain and time-domain features are extracted from EEG, and time-domain features are obtained from fNIRS signals. Then, a frequency-domain graph convolutional network (FGCN) and a time-domain convolutional network (TCNN) are deployed in parallel. The EEG feature views from different frequency bands are modeled using an FGCN module to capture graph-structured relationships, while the time-domain views of EEG and fNIRS are processed by a TCNN module to extract spatial and temporal features. Finally, a cross-attention fusion network (CAF) is applied to achieve interactive fusion of multimodal features. Experiments demonstrate that the proposed multi-view EEG approach achieves higher recognition accuracy compared to using only the EEG view. Additionally, the mmultimodalrecognition results outperform single-modal EEG and single-modal fNIRS by 1.73% and 6.65%, respectively. When compared with other emotion recognition models, the proposed method achieves the highest accuracy of 96.09%, proving its superior performance. Full article
(This article belongs to the Special Issue Applications of AI in Non-Invasive Biosensing Technologies)
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