Intelligent Method for Real-Time Portable EEG Artifact Annotation in Semiconstrained Environment Based on Computer Vision

Abstract

As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects’ activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant’s blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs.

A Lightweight Framework for Perception Analysis Based on Multimodal Cognition-Aware Computing

“…For example, Dlib (Suwarno and Kevin, 2020 ) can capture the facial features in the video in real time, OpenPose (Qiao et al, 2017 ) can analyze human actions in real time, and Meta-Updater (Dai et al, 2020 ) can track multiple targets in the video for a long time. Researchers have embraced these techniques as an adjunct to neuroscience and biobehavioral observations (Qian et al, 2022 ). Thus, the key information in the audio and video streams can be extracted from the stream and transformed into a new data structure, organized in time series.…”