A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43977137" target="_blank" >RIV/49777513:23520/25:43977137 - isvavai.cz</a>
Result on the web
<a href="https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1716204/full" target="_blank" >https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1716204/full</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/fnins.2025.1716204" target="_blank" >10.3389/fnins.2025.1716204</a>
Alternative languages
Result language
angličtina
Original language name
A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection
Original language description
Introduction: Motor imagery (MI) classification and sleep apnea (SA) detection are two critical tasks in brain-computer interface (BCI) and biomedical signal analysis. Traditional deep learning models have shown promise in these domains, but often struggle with temporal sparsity and energy efficiency, especially in real-time or embedded applications.Methods: In this study, we propose SpiTranNet, a novel architecture that deeply integrates Spiking Neural Networks (SNNs) with Transformers through Spiking Multi-Head Attention (SMHA), where spiking neurons replace standard activation functions within the attention mechanism. This integration enables biologically plausible temporal processing and energy-efficient computations while maintaining global contextual modeling capabilities. The model is evaluated across three physiological datasets, including one electroencephalography (EEG) dataset for MI classification and two electrocardiography (ECG) datasets for SA detection.Results: Experimental results demonstrate that the hybrid SNN-Transformer model achieves competitive accuracy compared to conventional machine learning and deep learning models.Discussion: This work highlights the potential of neuromorphic-inspired architectures for robust and efficient biomedical signal processing across diverse physiological tasks.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Frontiers in Neuroscience
ISSN
1662-453X
e-ISSN
1662-453X
Volume of the periodical
19
Issue of the periodical within the volume
DEC 12 2025
Country of publishing house
CH - SWITZERLAND
Number of pages
14
Pages from-to
1-14
UT code for WoS article
001648529300001
EID of the result in the Scopus database
2-s2.0-105026813030