EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss

Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorith...

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Detalles Bibliográficos
Autores principales: Luo, Tian-jian, Fan, Yachao, Chen, Lifei, Guo, Gongde, Zhou, Changle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204859/
https://www.ncbi.nlm.nih.gov/pubmed/32425763
http://dx.doi.org/10.3389/fninf.2020.00015

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204859/
https://www.ncbi.nlm.nih.gov/pubmed/32425763
http://dx.doi.org/10.3389/fninf.2020.00015

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