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Una neuroprótesis de habla de alto rendimiento

Francis R Willett1, Erin M Kunz2,3, Chaofei Fan4

  • 1Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA. willett2@gmail.com.

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|August 23, 2023
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce una nueva interfaz de habla cerebro-computadora (BCI) que decodifica el intento de habla a partir de señales neuronales. Este BCI avanzado mejora significativamente la precisión y la velocidad para la restauración de la comunicación en individuos con parálisis.

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Área de la Ciencia:

  • La neurociencia
  • Ingeniería biomédica
  • Tecnología de rehabilitación

Sus antecedentes:

  • Las interfaces cerebro-ordenador (BCI) ofrecen una vía de comunicación potencial para las personas con parálisis severa.
  • Las IC del habla existentes tienen limitaciones en la precisión y el tamaño del vocabulario, lo que dificulta la comunicación efectiva.
  • La esclerosis lateral amiotrófica (ELA) afecta progresivamente el habla, lo que requiere tecnologías de asistencia avanzadas.

Objetivo del estudio:

  • Desarrollar y evaluar un BCI de alto rendimiento de voz a texto utilizando grabaciones de microelectrodos intracorticales.
  • Para evaluar la precisión y la velocidad del BCI en la decodificación del intento de habla de un participante con ELA.
  • Investigar los principios de codificación neuronal relevantes para el desarrollo del BCI del habla.

Principales métodos:

  • Utilizó matrices de microelectrodos intracorticales para registrar el aumento de la actividad neuronal durante el intento de hablar.
  • Desarrolló algoritmos de decodificación para traducir señales neuronales en texto.
  • Evaluación del rendimiento utilizando tasas de error de palabras en diferentes tamaños de vocabulario y velocidad de decodificación medida.

Principales resultados:

  • Logró una tasa de error del 9,1% en un vocabulario de 50 palabras, una mejora de 2,7 veces con respecto al estado de la técnica anterior.
  • Demostró la primera decodificación exitosa de vocabulario grande (125.000 palabras) con una tasa de error de palabra del 23,8%.
  • Alcanzó una velocidad de decodificación de 62 palabras por minuto, 3.4 veces más rápido que los registros anteriores.

Conclusiones:

  • Las grabaciones neuronales de alta resolución permiten una decodificación precisa y rápida del habla a través de BCIs.
  • El código neural para el habla contiene características que facilitan la decodificación robusta incluso después de una parálisis prolongada.
  • Esta tecnología BCI presenta un enfoque viable para restaurar la comunicación para las personas que no hablan.