Cargando…
An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth
Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In this study, we compared multiple model explan...
Autores principales: | Sujatha Ravindran, Akshay, Contreras-Vidal, Jose |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584975/ https://www.ncbi.nlm.nih.gov/pubmed/37853010 http://dx.doi.org/10.1038/s41598-023-43871-8 |
Ejemplares similares
-
An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding
por: Nakagome, Sho, et al.
Publicado: (2020) -
Empirical comparison of deep learning methods for EEG decoding
por: de Oliveira, Iago Henrique, et al.
Publicado: (2023) -
Ground Truth
por: Garrity, George M.
Publicado: (2009) -
Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence
por: Cruz-Garza, Jesus G., et al.
Publicado: (2020) -
Simultaneous human intracerebral stimulation and HD-EEG, ground-truth for source localization methods
por: Mikulan, Ezequiel, et al.
Publicado: (2020)