.png){kind=icon}
返回
Quantitative analysis of task selection for brain-computer interfaces
.png){kind=icon}
OA
AI
DOI：10.1088/1741-2560/11/5/056002.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Objective. To assess quantitatively the impact of task selection in the performance of brain-computer interfaces (BCI). Approach. We consider the task-pairs derived from multi-class BCI imagery movement tasks in three different datasets. We analyze for the first time the benefits of task selection on a large-scale basis (109 users) and evaluate the possibility of transferring task-pair information across days for a given subject. Main results. Selecting the subject-dependent optimal task-pair among three different imagery movement tasks results in approximately 20% potential increase in the number of users that can be expected to control a binary BCI. The improvement is observed with respect to the best task-pair fixed across subjects. The best task-pair selected for each subject individually during a first day of recordings is generally a good task-pair in subsequent days. In general, task learning from the user side has a positive influence in the generalization of the optimal task- pair, but special attention should be given to inexperienced subjects. Significance. These results add significant evidence to existing literature that advocates task selection as a necessary step towards usable BCIs. This contribution motivates further research focused on deriving adaptive methods for task selection on larger sets of mental tasks in practical online scenarios.
Keyword:
brain-computer interface
task selection
subject learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
4.0K
被引数:
1.4W
机构
引用论文
Synthesis of 3-vinylpiperidines, 3-ethylidenepiperidines and 5-functionalized-1,2,3,4-tetrahydropyridines.png)
Tetrahedron
TetrahedronIF0
PREAI
分享收藏分享收藏
查看更多内容