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Un método de aprendizaje auto-supervisado basado en el conocimiento para mejorar el reconocimiento de emociones

Hanqi Wang1, Jingyu Zhang1, Peng Ye2

  • 1College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.

Neural networks : the official journal of the International Neural Network Society
|February 10, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un nuevo marco de aprendizaje auto-supervisado para el reconocimiento de emociones utilizando interfaces cerebro-ordenador (BCI) de electroencefalografía (EEG). El método mejora el reconocimiento de emociones al preservar datos clave y reducir la variabilidad entre individuos.

Palabras clave:
EEG reconocimiento de emociones reconocimiento de emocionesEl conocimiento impulsado por el conocimiento.Aprendizaje auto-supervisado de aprendizaje.

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Área de la Ciencia:

  • La neurociencia es la neurociencia.
  • Ciencias de la computación Ciencias de la computación
  • La inteligencia artificial es la inteligencia artificial.

Sus antecedentes:

  • El reconocimiento de emociones mediante electroencefalografía (EEG) y interfaces cerebro-ordenador (BCI) es vital para la interacción humano-ordenador, la medicina y la neurociencia.
  • Los datos EEG etiquetados limitados obstaculizan el progreso, por lo que el aprendizaje auto-supervisado es una alternativa prometedora.
  • Los métodos auto-supervisados existentes luchan por preservar la información relacionada con las emociones y superar la variabilidad inter-sujeto.

Objetivo del estudio:

  • Proponer un nuevo marco de aprendizaje auto-supervisado basado en el conocimiento para el reconocimiento de emociones basado en EEG.
  • Para abordar los desafíos de preservar la información relacionada con las emociones y reducir la variabilidad inter-sujeto.
  • Mejorar el rendimiento y la generalización de los sistemas de reconocimiento de emociones EEG.

Principales métodos:

  • Un marco de aprendizaje auto-supervisado basado en el conocimiento que incorpora el conocimiento del dominio para aproximar la extracción de entropía diferencial (DE).
  • Un marco en cascada de dos componentes: aprendizaje de entropía diferencial convolucional multi-rama (MCDEL) y alineación de entropía contrastiva (CEA).
  • MCDEL simula la extracción de DE para preservar la información relacionada con las emociones y generalizable; CEA expone la semántica emocional compleja en el espacio de alta dimensión.

Principales resultados:

  • El método propuesto logró un rendimiento superior en comparación con los enfoques auto-supervisados existentes.
  • La precisión media independiente del sujeto alcanzó el 84.48% ± 5.79% en SEED y el 67.64% ± 6.35% (Arousal) y el 68.63% ± 7.77% (Valence) en DREAMER.
  • Los estudios de ablación confirmaron la contribución de cada componente, y la visualización t-SNE demostró una reducción de la variabilidad entre sujetos y una mejora de la discriminación emocional.

Conclusiones:

  • El nuevo marco de auto-supervisión impulsado por el conocimiento aborda efectivamente los desafíos clave en el reconocimiento de emociones EEG.
  • El método demuestra mejoras significativas en la precisión y la generalización, superando a las técnicas existentes de auto-supervisión.
  • El marco muestra una promesa para el avance de la interacción humano-computadora, la medicina y la neurociencia a través de un reconocimiento de emociones más robusto.