Han, Yiyuan and Ziebell, Philipp and Riccio, Angela and Halder, Sebastian (2022) Two sides of the same coin: adaptation of BCIs to internal states with user-centered design and electrophysiological features. Brain-Computer Interfaces, 9 (2). pp. 102-114. DOI https://doi.org/10.1080/2326263x.2022.2041294
Han, Yiyuan and Ziebell, Philipp and Riccio, Angela and Halder, Sebastian (2022) Two sides of the same coin: adaptation of BCIs to internal states with user-centered design and electrophysiological features. Brain-Computer Interfaces, 9 (2). pp. 102-114. DOI https://doi.org/10.1080/2326263x.2022.2041294
Han, Yiyuan and Ziebell, Philipp and Riccio, Angela and Halder, Sebastian (2022) Two sides of the same coin: adaptation of BCIs to internal states with user-centered design and electrophysiological features. Brain-Computer Interfaces, 9 (2). pp. 102-114. DOI https://doi.org/10.1080/2326263x.2022.2041294
Abstract
The ideal brain–computer interface (BCI) adapts to the user’s state to enable optimal BCI performance. Two methods of BCI adaptation are commonly applied: User-centered design (UCD) responds to individual user needs and requirements. Passive BCIs can adapt via online analysis of electrophysiological signals. Despite similar goals, these methods are rarely discussed in combination. Hence, we organized a workshop for the 8th International BCI Meeting 2021 to discuss the combined application of both methods. Here we expand upon the workshop by discussing UCD in more detail regarding its utility for end-users as well as non-end-user-based early-stage BCI development. Furthermore, we explore electrophysiology-based online user state adaptation concerning consciousness and pain detection. The integration of the numerous BCI user state adaptation methods into a unified process remains challenging. Yet, further systematic accumulation of specific knowledge about assessment and integration of internal user states bears great potential for BCI optimization.
Item Type: | Article |
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Uncontrolled Keywords: | EEG; BCI; UCD; signal diversity; functional connectivity |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 08 Mar 2022 15:17 |
Last Modified: | 30 Oct 2024 21:17 |
URI: | http://repository.essex.ac.uk/id/eprint/32464 |
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Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0