A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Neuroscience

ISSN

1662-453X

e-ISSN

1662-453X

Svazek periodika

19

Číslo periodika v rámci svazku

DEC 12 2025

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

001648529300001

EID výsledku v databázi Scopus

2-s2.0-105026813030