We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An asynchronous artifact-enhanced electroencephalogram based control paradigm assisted by slight facial expression.
- Authors
Zhufeng Lu; Xiaodong Zhang; Hanzhe Li; Teng Zhang; Linxia Gu; Qing Tao
- Abstract
In this study, an asynchronous artifact-enhanced electroencephalogram (EEG)-based control paradigm assisted by slight-facial expressions (sFEparadigm) was developed. The brain connectivity analysis was conducted to reveal the dynamic directional interactions among brain regions under sFEparadigm. 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 sFEparadigm focused on the mainstream defect as the insufficiency of realtime 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 sFEparadigm, enabling a novel solution to the non-invasive EEG-based control in real-world challenges.
- Subjects
FACIAL expression; ARTIFICIAL hands; ELECTROENCEPHALOGRAPHY; SIGNAL processing; BRAIN-computer interfaces; JOYSTICKS; MEDICAL artifacts
- Publication
Frontiers in Neuroscience, 2022, Vol 16, p1
- ISSN
1662-4548
- Publication type
Article
- DOI
10.3389/fnins.2022.892794