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Aprendizaje de espacios latentes neuronales discretos para la decodificación del habla de alto rendimiento
1Affiliated Mental Health Center Hangzhou Seventh People's Hospital, and MOE Frontier Science Center for Brain Science and Brain-machine Integration, and College of Computer Science and Technology, Zhejiang University, 38#, Zheda Road, Hangzhou, Zhejiang province, 310058, CHINA.
Journal of neural engineering
|December 15, 2025
Resumen
Este estudio presenta un novedoso espacio latente neuronal discreto para interfaces cerebro-computadora (ICC) de habla. Este enfoque mejora la precisión y la robustez de la decodificación del habla para personas con afasia.
Área de la Ciencia:
- Neurociencia
- Ingeniería Biomédica
- Inteligencia Artificial
Sus antecedentes:
- Las interfaces cerebro-computadora (ICC) de habla ofrecen una vía de comunicación para personas con afasia.
- El aprendizaje de la representación neuronal es crucial para decodificar el habla a partir de señales cerebrales.
Objetivo del estudio:
- Desarrollar un enfoque novedoso para la decodificación del habla utilizando espacios latentes neuronales discretos.
- Mejorar la precisión y la robustez de las ICC de habla.
Principales métodos:
- Se propuso una red de aprendizaje de representación cuantificada para aprender espacios latentes neuronales discretos.
- Se utilizaron señales de electroencefalografía estereotáctica intracraneal (sEEG) de 11 sujetos para los experimentos.
Principales resultados:
- El método propuesto mejoró significativamente la precisión de la decodificación del habla.
- Demostró una mayor robustez en la decodificación del habla en comparación con los métodos existentes.
Conclusiones:
- El enfoque del espacio latente neuronal discreto tiene un potencial significativo para avanzar en las ICC de habla.
- Este método puede mejorar la funcionalidad y la usabilidad de las ICC para personas con afasia.
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