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

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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Bibliographic Details
Main Authors: Abdelkader Dairi, Nabil Zerrouki, Fouzi Harrou, Ying Sun
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Diagnostics
Subjects:
deep learning
motor imagery
Isolation Forest
anomaly detection
EEG signals classification
Online Access:https://www.mdpi.com/2075-4418/12/12/2984

Internet

https://www.mdpi.com/2075-4418/12/12/2984

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