Two-channel in-ear EEG system for detection of visuomotor tracking state: A preliminary study

Abstract

Evaluating an operator's mental workload during work activities is crucial to maintain safety and performance. By minimizing human error associated with work demands, especially in a hazardous environment, potentially serious errors may be avoided. This study aims to assess the feasibility of using an in-ear EEG system to classify the user's state in a visuomotor tracking task that may influence mental workload and motor action. A two-channel wireless in-ear EEG system was used to record EEG signals while subjects performed the task using a joystick to manipulate an object displayed on a monitor. A highly comparative time series analysis was employed on the processed signals to extract and select the top features for each subject individually. The features sets were trained and tested with support vector machines, random forest, linear discriminant analysis, subspace discriminant, and neural network to compare their performances. Models trained on two trials, each 14 minutes in duration and tested on the other trial were able to yield an accuracy of 79.30 ± 4.85% on average across the ten participants with an individualized moving average threshold filter and classifier. This proof-of-concept study demonstrates the feasibility of using a two-channel wireless in-ear EEG system as a viable solutions to develop wearable devices to detect mental workload associated with the execution of visuomotor tasks.