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How different immersive environments affect intracortical brain computer interfaces

delete2025-02-10
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OA
AI
A
Ariana F. Tortolani
N
Nicolas G. Kunigk
A
Anton R. Sobinov
M
Michael L. Boninger
S
Sliman J. Bensmaı̈a
J
Jennifer L. Collinger
N
Nicholas G. Hatsopoulos
J
John E. Downey *
DOI:10.1088/1741-2552/adb078delete
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摘要

摘要

En 中文
Objective. As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Approach. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor. Main results. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered. Significance. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.Clinical Trial: NCT01894802
Keyword:
intracortical BCI
virtual environment
motor cortex
motor learning
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期刊

Journal of Neural Engineering 封面图
Journal of Neural Engineering
IF:
3.8
论文数:
4.0K
被引数:
1.4W

机构

U
university of chicago
学者数:
4.5W
论文数: 3.7W
被引数: 80
P
pennsylvania commonwealth system of higher education (pcshe)
学者数:
12.9W
论文数: 11.7W
被引数: 177
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