Generative adversarial networks-based data augmentation for brain-computer interface

• المؤلفون الرئيسيون: Fahimi, Fatemeh, Dosen, Strahinja, Ang, Kai Keng, Mrachacz-Kersting, Natalie, Guan, Cuntai
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Computer science and engineering; Brain-Computer Interface; Data Augmentation
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/159616
• المؤسسة: Nanyang Technological University

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 diverted...