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.
Highlights
Electroencephalography (EEG) has been proved to be a useful methodological tool for understanding brain activities, including the processes of perception, cognition, and decision, which are the basis of daily behaviors, business, and engineering activities.With the great attention to human decision-making and the recognition of limitations of traditional psychological/ self-reported driven approaches [1,2,3,4], the neuromanagement on revealing the mechanism of human’s behavior and decision-making based on brain imaging technology is promoted [5,6,7]
(2) We propose an intelligence method based on computer vision to automatically annotate the time segments and categories of artifacts caused by blinks and head movements in real time, which is of great importance for the large-scale application and realtime analysis of portable EEG technology (PEEGT) and traditional EEG
Work e method proposed by this paper changed the original artifact postprocessing mode based on signal recognition to the artifact preprocessing mode based on behavior recognition by computer vision (CV), which combined and optimized three efficient computer recognition algorithms. e paper proved the method’s effectiveness in the experiment. rough real-time monitoring of the participant’s facial signals, the intelligent system can identify two main antecedent causes of the EEG artifacts, participant’s blinks and head movements, and annotate the artifacts’ time segments in real time
Summary
Electroencephalography (EEG) has been proved to be a useful methodological tool for understanding brain activities, including the processes of perception, cognition, and decision, which are the basis of daily behaviors, business, and engineering activities.With the great attention to human decision-making and the recognition of limitations of traditional psychological/ self-reported driven approaches [1,2,3,4], the neuromanagement on revealing the mechanism of human’s behavior and decision-making based on brain imaging technology is promoted [5,6,7]. Electroencephalography (EEG) has been proved to be a useful methodological tool for understanding brain activities, including the processes of perception, cognition, and decision, which are the basis of daily behaviors, business, and engineering activities. Due to the high cost of purchasing and maintaining neurometric equipment and the complex operation and data analysis mode, brain technologies are limited to the laboratory environments and hindered from becoming widespread. Benefiting from the development of portable EEG technology (PEEGT), devices become cheaper and smaller, such as single-electrode. E utilization of PEEGT significantly expands the application of neurophysiological measurements and dramatically increases the practicability of neurometric equipment, such as in marketing [5], management [9], education [10], and engineering [11] More and more research and commercial applications use PEEGT as a measurement tool. e utilization of PEEGT significantly expands the application of neurophysiological measurements and dramatically increases the practicability of neurometric equipment, such as in marketing [5], management [9], education [10], and engineering [11]
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