EEG-Based Mental Tasks Recognition via a Deep Learning-Driven Anomaly Detector

This paper introduces an unsupervised deep learning-driven scheme for mental tasks’ recognition using EEG signals. To this end, the Multichannel Wiener filter was first applied to EEG signals as an artifact removal algorithm to achieve robust recognition. Then, a quadratic time-frequency distributio...

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Detalles Bibliográficos
Autores principales: Dairi, Abdelkader, Zerrouki, Nabil, Harrou, Fouzi, Sun, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776901/
https://www.ncbi.nlm.nih.gov/pubmed/36552991
http://dx.doi.org/10.3390/diagnostics12122984

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776901/
https://www.ncbi.nlm.nih.gov/pubmed/36552991
http://dx.doi.org/10.3390/diagnostics12122984

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