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Detección de carga cognitiva por lóbulos mediante descomposición de Fourier empírica y aprendizaje automático

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Este estudio presenta un método de Descomposición de Fourier Empírica (EMFD) combinado con Aprendizaje Automático de Conjunto Optimizado (OML) para la detección precisa de la carga cognitiva basada en electroencefalografía (EEG), logrando una precisión superior al 97%.

Palabras clave:
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Área de la Ciencia:

  • Neurociencia
  • Interacción Humano-Computadora
  • Aprendizaje Automático

Sus antecedentes:

  • La carga cognitiva influye en la actividad neuronal, lo que requiere una evaluación precisa en neurociencia e Interacción Humano-Computadora (HCI).
  • La electroencefalografía (EEG) ofrece un método no invasivo para monitorizar las respuestas cerebrales al esfuerzo mental.

Objetivo del estudio:

  • Explorar la extracción y clasificación de características basadas en EEG para una evaluación precisa de la carga cognitiva.
  • Evaluar la eficacia de la Descomposición de Fourier Empírica (EMFD) y el Aprendizaje Automático de Conjunto Optimizado (OML) para la detección de la carga cognitiva.

Principales métodos:

  • Las señales de EEG se descompusieron utilizando EMFD en funciones de modo intrínseco.
  • Se extrajeron y redujeron características basadas en entropía.
  • Las clasificaciones se realizaron utilizando OML y clasificadores de aprendizaje automático (ML) convencionales en datos por lóbulos y generales.
  • El método se validó en los conjuntos de datos de la Tarea de Aritmética Mental (MAT) y la Visión Múltiple Transcrómica Espacial (STEW).

Principales resultados:

  • El marco OML basado en EMFD logró altas precisiones de clasificación: 97,8% en MAT y 96,4% en STEW.
  • El análisis por lóbulos demostró un fuerte rendimiento en todas las regiones cerebrales.
  • El lóbulo frontal arrojó las precisiones más altas, alcanzando 97,8% (MAT) y 96,08% (STEW).

Conclusiones:

  • La EMFD combinada con OML mejora eficazmente la detección de la carga cognitiva basada en EEG.
  • El rendimiento constante del marco en los conjuntos de datos confirma su robustez.
  • Los hallazgos resaltan el papel significativo del lóbulo frontal en el procesamiento cognitivo y el potencial del método para aplicaciones en el mundo real.