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Deep learning methods for motor imagery detection from raw EEG : applications to brain-computer interfaces

Oleksii Avilov 1, 2
1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : This thesis presents three contributions to improve the recognition of motor imaginary movements used by numerous brain-computer interfaces (BCI) as types of interaction. First of all, we propose to estimate the quality of motor images by detecting outliers and removing them before training. Next, we study the feature selection for seven different motor imaginary movements. Finally, we present a deep learning architecture based on the principles of EEGNet network applied directly on raw electroencephalographic signals and adapted to the number of electrodes. We show in particular its benefits for improving the detection of intraoperative awareness and other applications.
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https://hal.univ-lorraine.fr/tel-03229010
Contributor : Thèses Ul <>
Submitted on : Tuesday, May 18, 2021 - 3:41:17 PM
Last modification on : Wednesday, May 19, 2021 - 3:33:29 AM

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  • HAL Id : tel-03229010, version 1

Citation

Oleksii Avilov. Deep learning methods for motor imagery detection from raw EEG : applications to brain-computer interfaces. Computer Science [cs]. Université de Lorraine; Institut polytechnique de Kiev (Ukraine), 2021. English. ⟨NNT : 2021LORR0032⟩. ⟨tel-03229010⟩

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