Toward calibration-free motor imagery brain–computer interfaces: a VGG-based convolutional neural network and WGAN approach
A G Habashi,
Ahmed M Azab,
Seif Eldawlatly
et al.
Abstract:Objective. Motor Imagery (MI) represents one major paradigm of Brain-Computer Interfaces (BCIs) in which users rely on their Electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technolo… Show more
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