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

Polymer Classification: Architecture01:14

Polymer Classification: Architecture

3.9K
Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
3.9K
Arithmetic Mean01:08

Arithmetic Mean

17.9K
The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”
When all the values in a data set are not unique, the sum in the numerator can be calculated by multiplying each distinct value by its frequency.
Sometimes, the arithmetic mean of a sample can be affected by a few data points...
17.9K
Arithmetic Sequences01:30

Arithmetic Sequences

240
An arithmetic sequence is a structured arrangement of numbers where each term is derived by adding a constant value, known as the common difference, to the previous term. This consistent pattern allows for the efficient computation of any term within the sequence as well as the cumulative sum of multiple terms. The formula for finding the nth term of an arithmetic sequence is:Here, aₙ represents the nth term of the sequence, a is the first term, d is the common difference, and n is the...
240
Phasor Arithmetics01:13

Phasor Arithmetics

840
Phasors and their corresponding sinusoids are interrelated, offering unique insights into the behavior of alternating current (AC) circuits. One way to understand this relationship is through the operations of differentiation and integration in both the time and phasor domains.
When the derivative of a sinusoid is taken in the time domain, it transforms into its corresponding phasor multiplied by j-omega (jω) in the phasor domain, where j is the imaginary unit, and ω is the angular...
840
Hybrid Zones02:29

Hybrid Zones

22.0K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
22.0K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

429
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
429

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Correction: Advanced EEG signal classification for neural prosthetic devices using metaheuristic and deep learning techniques.

Frontiers in digital health·2026
Same author

Advanced EEG signal classification for neural prosthetic devices using metaheuristic and deep learning techniques.

Frontiers in digital health·2026
Same author

Multi-domain feature extraction and Sand Cat Swarm Optimized Broad Learning System for EEG-based Motor Imagery decoding in stroke patients.

Computers in biology and medicine·2025
Same author

A hybrid quorum sensing model for neurodynamic feature optimization in EEG-based Parkinson's disease detection.

Computers in biology and medicine·2025
Same author

Deep feature extraction and swarm-optimized enhanced extreme learning machine for motor imagery recognition in stroke patients.

Journal of neuroscience methods·2025
Same author

Method for detecting rough road index using iot sensor fusion and v2v interaction for efficient road infrastructure management.

MethodsX·2025

Video Experimental Relacionado

Updated: Feb 10, 2026

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory
08:16

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Published on: May 11, 2020

9.0K

Optimización de la arquitectura profunda de CNN mediante el algoritmo aritmético híbrido de Harris Hawks para la

Soniya Shakil Usgaonkar1, Damodar Reddy Edla2, Dharavath Ramesh3

  • 1Department of Computer Science and Engineering, National Institute of Technology, Cuncolim, 403 703, Goa, India; Information Technology Department, Goa College of Engineering, Farmagudi, Ponda, 403401, Goa, India.

Neuroscience
|February 8, 2026
PubMed
Resumen

Este estudio presenta un novedoso marco para clasificar estados de meditación utilizando señales de electroencefalografía (EEG). El modelo híbrido Harris Hawks Optimization-Arithmetic Optimization Algorithm-Convolutional Neural Network (HHO-AOA-CNN) logró una precisión del 94,20 % en la distinción de tipos de meditación.

Palabras clave:
aprendizaje profundoelectroencefalografíaalgoritmo híbridomeditación

Más Videos Relacionados

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement
06:58

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement

Published on: June 25, 2016

20.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K

Videos de Experimentos Relacionados

Last Updated: Feb 10, 2026

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory
08:16

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Published on: May 11, 2020

9.0K
Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement
06:58

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement

Published on: June 25, 2016

20.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K

Área de la Ciencia:

  • Neurociencia
  • Inteligencia Artificial
  • Procesamiento de Señales

Sus antecedentes:

  • La meditación mejora la función cognitiva, pero el análisis de señales de EEG para la clasificación sigue siendo un desafío.
  • Los métodos existentes utilizan características limitadas y aprendizaje automático tradicional, careciendo de técnicas avanzadas.
  • Existe la necesidad de enfoques integrados que combinen análisis de tiempo-frecuencia, aprendizaje profundo y optimización para la clasificación de meditación EEG.

Objetivo del estudio:

  • Desarrollar un marco híbrido basado en EEG para clasificar estados de meditación.
  • Mejorar la precisión de la clasificación de la meditación integrando técnicas avanzadas de optimización y aprendizaje profundo.
  • Abordar las limitaciones en el análisis de señales de EEG actuales para la investigación de la meditación.

Principales métodos:

  • Se desarrolló un marco híbrido que combina Harris Hawks Optimization (HHO) y Arithmetic Optimization Algorithm (AOA) para ajustar los parámetros de la Red Neuronal Convolucional (CNN).
  • Las señales de EEG se preprocesaron y transformaron en imágenes de tiempo-frecuencia utilizando la Transformada de Stockwell (S-transform).
  • El modelo HHO-AOA-CNN procesó estas imágenes para la optimización de hiperparámetros y la clasificación de los estados Vipassana (VIP), Isha Shoonya (IS) y Control (CTR).

Principales resultados:

  • El marco HHO-AOA-CNN propuesto logró una precisión de clasificación del 94,20 %.
  • El modelo híbrido demostró un rendimiento superior en comparación con los modelos HHO-CNN, AOA-CNN y CNN independientes.
  • El análisis estadístico confirmó la estabilidad y robustez del enfoque de optimización híbrida.

Conclusiones:

  • El marco HHO-AOA-CNN desarrollado ofrece un método robusto y preciso para la clasificación de la meditación basada en EEG.
  • Este enfoque integra eficazmente técnicas avanzadas de procesamiento de señales, aprendizaje profundo y optimización.
  • Los hallazgos contribuyen a una mejor comprensión y medición objetiva de los estados de meditación a través del análisis de EEG.