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...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/92e7311fbe7145d8a12d8903b24cf3ed |
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| Sumario: | 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 insight into patterns of cortical networks. |
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