Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Nuclear Fusion02:45

Nuclear Fusion

33.8K
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
33.8K
pH Scale02:41

pH Scale

79.5K
Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
79.5K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Scaling01:26

Scaling

593
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
593

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Molecular mechanisms of Buyang Huanwu Decoction in improving Diabetic kidney disease based on network pharmacology and experimental validation.

American journal of translational research·2026
Same author

PRMT5 inhibition by SCR-6920 downregulates HIF-1α and exhibits synergistic antitumor activity with bevacizumab in ovarian cancer.

Molecular cancer therapeutics·2026
Same author

PRMT5 Inhibition by SCR-6920 Downregulates HIF-1α and Exhibits Synergistic Antitumor Activity with Bevacizumab in Ovarian Cancer.

Molecular cancer therapeutics·2026
Same author

A Domain Generalization Method for EEG Based on Domain-Invariant Feature and Data Augmentation.

Cyborg and bionic systems (Washington, D.C.)·2026
Same author

Discovery and Preclinical Evaluations of Potent, Selective, and Allosteric Covalent WRN Inhibitors with Improved PK Properties.

ACS medicinal chemistry letters·2026
Same author

HCFNet: A heterogeneous frequency bands coupling CNN for enhanced short-time fast response in motor imagery decoding.

Journal of neuroscience methods·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Jan 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

MS-TSEFNet: Red Neuronal de Fusión Eficiente de Características Espaciotemporales Multiescala

Weijie Wu1, Lifei Liu1, Weijie Chen1

  • 1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta una nueva red de aprendizaje profundo para decodificar imágenes motoras utilizando señales de electroencefalograma (EEG). El MS-TSEFNet propuesto mejora la fusión de características para una mayor precisión en las interfaces cerebro-computadora.

Palabras clave:
interfaz cerebro-computadoraredes neuronales convolucionalesimagen motora

Más Videos Relacionados

Co-expression of Multiple Chimeric Fluorescent Fusion Proteins in an Efficient Way in Plants
09:45

Co-expression of Multiple Chimeric Fluorescent Fusion Proteins in an Efficient Way in Plants

Published on: July 1, 2018

10.2K
Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay
05:16

Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay

Published on: July 26, 2021

2.0K

Videos de Experimentos Relacionados

Last Updated: Jan 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
Co-expression of Multiple Chimeric Fluorescent Fusion Proteins in an Efficient Way in Plants
09:45

Co-expression of Multiple Chimeric Fluorescent Fusion Proteins in an Efficient Way in Plants

Published on: July 1, 2018

10.2K
Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay
05:16

Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay

Published on: July 26, 2021

2.0K

Área de la Ciencia:

  • Neurociencia
  • Ciencias de la Computación
  • Ingeniería Biomédica

Sus antecedentes:

  • La decodificación de imágenes motoras (MI) utilizando electroencefalograma (EEG) es crucial para las interfaces cerebro-computadora (BCI).
  • Los modelos actuales de aprendizaje profundo luchan por fusionar eficazmente características multinivel en señales EEG complejas, lo que limita el rendimiento de la clasificación.
  • Existe la necesidad de modelos avanzados que puedan capturar la dinámica espaciotemporal e integrar información entre diferentes niveles de características.

Objetivo del estudio:

  • Proponer una nueva red de aprendizaje profundo, la Red de Fusión Eficiente de Características Espaciotemporales Multiescala (MS-TSEFNet), para mejorar la decodificación de imágenes motoras a partir de señales EEG.
  • Mejorar la fusión de características multinivel extraídas de datos EEG.
  • Mejorar la precisión y robustez de las BCI basadas en EEG.

Principales métodos:

  • Se desarrolló MS-TSEFNet incorporando módulos de convolución multiescala para capturar la dinámica temporal en varias escalas de tiempo.
  • Se integró un mecanismo de atención espacial para identificar eficazmente las correlaciones espaciales entre los electrodos EEG.
  • Se empleó una estrategia eficiente de fusión de características para integrar profundamente las características de diferentes niveles, mejorando la expresividad del modelo.

Principales resultados:

  • MS-TSEFNet logró altas precisiones de clasificación en conjuntos de datos públicos: 80,31 % (BCIC-IV2a), 86,69 % (BCIC-IV2b) y 71,14 % (ECUST).
  • La red propuesta demostró un rendimiento superior en comparación con los algoritmos actuales de última generación.
  • Los estudios de ablación confirmaron la contribución significativa de cada módulo, particularmente los módulos de convolución multiescala y fusión de características, al rendimiento general.

Conclusiones:

  • MS-TSEFNet decodifica eficazmente las señales de imágenes motoras aprovechando la extracción y fusión de características espaciotemporales multiescala.
  • La red ofrece una mayor precisión y robustez para las interfaces cerebro-computadora basadas en EEG.
  • Los hallazgos resaltan la importancia de las técnicas avanzadas de fusión de características para el procesamiento de señales EEG complejas.