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 obvious non-sFE-EEGs exclusion, interface 'ON' detection, sFE-EEGs real-time decoding, and validity judgment. 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 interface ON detection and 92.68% +/- 1.21 for sFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This sFE-paradigm was applied to two online manipulations for evaluating stability and agility. In object-moving with a robotic arm, the averaged intersection-over-union was 60.03 +/- 11.53%. In water-pouring with a prosthetic hand, the average water volume was 202.5 +/- 7.0 ml. During online, the sFE-paradigm performed no significant difference (P = 0.6521 and P = 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.

Automated summary

In this study, an asynchronous artifact-enhanced EEG-based control paradigm assisted by slight-facial expressions was developed. The brain connectivity analysis revealed dynamic interactions among brain regions. The paradigm showed high real-time capability and accuracy, demonstrating stability and agility in offline assessment and online manipulations.