Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
Andreu-Perez et al developed a multivariate pattern analysis for fNIRS data (xMVPA), which is powered by eXplainable Artificial Intelligence (XAI). They demonstrated its application in the context of investigating visual and auditory processing in six-month-old infants and showed that it provided in...
Guardado en:
Autores principales: | Javier Andreu-Perez, Lauren L. Emberson, Mehrin Kiani, Maria Laura Filippetti, Hani Hagras, Silvia Rigato |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/92e7311fbe7145d8a12d8903b24cf3ed |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Functional near-infrared spectroscopy (fNIRS) of posterolateral cerebellum and prefrontal cortex for fNIRS-driven cerebellar tES – a case report
por: Shubh Mohan Singh, et al.
Publicado: (2021) -
Reliability of fNIRS for noninvasive monitoring of brain function and emotion in sheep
por: Matteo Chincarini, et al.
Publicado: (2020) -
Development of an Integrated EEG/fNIRS Brain Function Monitoring System
por: Manal Mohamed, et al.
Publicado: (2021) -
Hemodynamics of speech production: An fNIRS investigation of children who stutter
por: B. Walsh, et al.
Publicado: (2017) -
Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS
por: Mickaël Causse, et al.
Publicado: (2017)