Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface

The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be div...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2021-09, Vol.32 (9), p.4039-4051
Hauptverfasser:	Fahimi, Fatemeh, Dosen, Strahinja, Ang, Kai Keng, Mrachacz-Kersting, Natalie, Guan, Cuntai
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adult Algorithms Artificial neural networks Attention Brain > computer interface (BCI) Brain-Computer Interfaces Calibration Classification Classifiers Computer Simulation Data acquisition Data augmentation deep learning diverted attention EEG electroencephalogram (EEG) Electroencephalography Electroencephalography - statistics & numerical data Female Gallium nitride Generative adversarial networks generative adversarial networks (GANs) Generators Healthy Volunteers Human-computer interface Humans Image generation Imagination intersubject transfer learning Machine Learning Male Mental task performance Neural networks Neural Networks, Computer Performance enhancement Psychomotor Performance Reproducibility of Results Task analysis Training Young Adult
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