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Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch.
- Source :
-
Journal of neural engineering [J Neural Eng] 2019 Jul 23; Vol. 16 (5), pp. 056001. Date of Electronic Publication: 2019 Jul 23. - Publication Year :
- 2019
-
Abstract
- Objective: Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to periodic tactile cues and this may challenge online control. In the present study, therefore, we implemented and evaluated a novel approach for online closed-loop control using the proposed BCI.<br />Approach: Eleven healthy subjects used the novel method to move a cursor in a 2D space. To assure robust control with properly timed commands, the BCI was integrated within a state machine allowing the subject to start the cursor movement in the selected direction and asynchronously stop the cursor. The brain switch was controlled using motor execution (ME) or imagery (MI) and the menu implemented four (straight movements) or eight commands (straight and diagonal movements).<br />Main Results: The results showed a high completion rate of a target hitting task (~97% and ~92% for ME and MI, respectively), with a small number of collisions, when four-channel control was used. There was no significant difference in outcome measures between MI and ME, and performance was similar for four and eight commands.<br />Significance: These results demonstrate that the novel state-based scheme driven by a robust BCI can be successfully utilized for online control. Therefore, it can be an attractive solution for providing the user an online-control interface with many commands, which is difficult to achieve using classic BCI solutions.
Details
- Language :
- English
- ISSN :
- 1741-2552
- Volume :
- 16
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- Journal of neural engineering
- Publication Type :
- Academic Journal
- Accession number :
- 31075785
- Full Text :
- https://doi.org/10.1088/1741-2552/ab20e5