Koning, Jamilla de (2022) A Motor Imagery-based Brain Computer Interface. Master thesis, Psychology.
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Abstract
Brain-Computer Interfaces (BCIs) are receiving more and more attention and interest in research. The applications of these systems range from medical and assistive (in stroke rehabilitation or paralyzed patients) to non-medical (videogames and virtual reality) (Lotte et al., 2015). One of the most used methods to control a BCI is motor imagery (MI). BCI performance is influenced by a variety of factors including classification algorithms, users’ states/traits and the way users are trained to perform a BCI task. The current study aims to test any difference between two newly created instructions modes (video vs written) and analyze important users’ characteristics (motivation and vividness of visual imagery) which are known to influence BCI performance. BCI performance was measured both with classification accuracy and subjective performance measures. 27 subjects were recruited for the study that consisted of two sessions in which kinesthetic motor imagery tasks (hand imagery and feet imagery) were performed. Additionally, participants had to fill out questionnaires regarding motivation, vividness of visual imagery, mind-body techniques, creative activities, and experience with electronic devices. Two RM-ANOVA were run to find any difference in BCI performance between the two instruction methods, additionally, correlation analyses were run between two independent variables motivation and vividness of visual imagery and BCI performance (classification accuracy and subjective performance). Results showed that no difference was present between the two instruction methods in BCI performance. Moreover, higher vividness of visual imagery was related to higher subjective performance. These findings might be useful for future research to find the most appropriate method of instructing participants and to design a BCI system adaptable to the needs of each participant.
| Item Type: | Thesis (Master) |
|---|---|
| Supervisor name: | Enriquez Geppert, S. |
| Degree programme: | Psychology |
| Differentiation route: | Clinical Neuropsychology (CN) [Master Psychology] |
| Date Deposited: | 16 Aug 2022 07:21 |
| Last Modified: | 16 Aug 2022 07:21 |
| URI: | http://gmwpublic.studenttheses.ub.rug.nl/id/eprint/1286 |
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