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A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

Title data

Penava, Pascal ; Büttner, Ricardo:
A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks.
In: IEEE Access. Vol. 11 (2023) . - pp. 118849-118864.
ISSN 2169-3536
DOI: https://doi.org/10.1109/ACCESS.2023.3326720

Official URL: Volltext

Project information

Project title:
Project's official title
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Project financing: Deutsche Forschungsgemeinschaft

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Brain-computer-interface; decision prediction; generative adversarial networks; motor imagery tasks; electroencephalography; machine learning
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science > Chair Business Administration XVIII - Information Systems Management and Data Science - Univ.-Prof. Dr. Ricardo Büttner
Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 23 Mar 2024 22:02
Last Modified: 25 Mar 2024 11:37
URI: https://eref.uni-bayreuth.de/id/eprint/89021