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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2022
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| Materias: | |
| 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 |