An asynchronous artifact-enhanced electroencephalogram based control paradigm assisted by slight facial expression.

In this study, an asynchronous artifact-enhanced electroencephalogram (EEG)-based control paradigm assisted by slight-facial expressions (sFE-paradigm) was developed. The brain connectivity analysis was conducted to reveal the dynamic directional interactions among brain regions under sFE-paradigm. The component analysis was applied to estimate the dominant components of sFE-EEG and guide the signal processing. Enhanced by the artifact within the detected electroencephalogram (EEG), the sFE-paradigm focused on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm contained four steps, including "," " "," and " It provided the asynchronous function, decoded eight instructions from the latest 100 ms signal, and greatly reduced the frequent misoperation. In the offline assessment, the sFE-paradigm achieved 96.46% ± 1.07 accuracy for " and 92.68% ± 1.21 for , with the theoretical output timespan less than 200 ms. This sFE-paradigm was applied to two online manipulations for evaluating stability and agility. In "," the averaged intersection-over-union was 60.03 ± 11.53%. In "," the average water volume was 202.5 ± 7.0 ml. During online, the sFE-paradigm performed no significant difference ( = 0.6521 and = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of sFE-paradigm, enabling a novel solution to the non-invasive EEG-based control in real-world challenges.