Closed-loop motor imagery EEG simulation for brain-computer interfaces.

ABSTRACT: In a brain-computer interface (BCI) system, the testing of decoding algorithms, tasks, and their parameters is critical for optimizing performance. However, conducting human experiments can be costly and time-consuming, especially when investigating broad sets of parameters. Attempts to utilize previously collected data in offline analysis lack a co-adaptive feedback loop between the system and the user present online, limiting the applicability of the conclusions obtained to real-world uses of BCI. As such, a number of studies have attempted to address this cost-wise middle ground between offline and live experimentation with real-time neural activity simulators. We present one such system which generates motor imagery electroencephalography (EEG) via forward modeling and novel mot

SUBMITTER: Shin H 

PROVIDER: S-EPMC9428352 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature