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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: EEG-based online regulation of difficulty in simulated flying
Authors: Jao, Ping-Keng
Chavarriaga, Ricardo
Millan, Jose del R.
et. al: No
DOI: 10.1109/TAFFC.2021.3059688
Published in: IEEE Transactions on Affective Computing
Issue Date: 2021
Publisher / Ed. Institution: IEEE
ISSN: 1949-3045
Language: English
Subject (DDC): 006: Special computer methods
150: Psychology
Abstract: Adaptively increasing the difficulty level in learning was shown to be beneficial than increasing the level after some fixed time intervals. To efficiently adapt the level, we aimed at decoding the subjective difficulty level based on EEG signals. We designed a visuomotor learning task that one needed to pilot a simulated drone through a series of waypoints of different sizes, to investigate the effectiveness of the EEG decoder. The EEG decoder was compared with another condition that the subjects decided when to increase the difficulty level. We examined the decoding performance together with behavioral outcomes. The online accuracies were higher than the chance level for 16 out of 26 cases, and the behavioral results, such as task scores, skill curves, and learning patterns, of EEG condition were similar to the condition based on manual regulation of difficulty.
Further description: ​© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

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