Adaptive Filtering for Improved EEG-Based Mental Workload Assessment of Ambulant Users.

Olivier Rosanne,Raymundo Cassani,Jean-François Gagnon,Tiago H Falk,Sebastien Tremblay,Isabela Albuquerque

doi:10.3389/fnins.2021.611962

Abstract

Recently, due to the emergence of mobile electroencephalography (EEG) devices, assessment of mental workload in highly ecological settings has gained popularity. In such settings, however, motion and other common artifacts have been shown to severely hamper signal quality and to degrade mental workload assessment performance. Here, we show that classical EEG enhancement algorithms, conventionally developed to remove ocular and muscle artifacts, are not optimal in settings where participant movement (e.g., walking or running) is expected. As such, an adaptive filter is proposed that relies on an accelerometer-based referential signal. We show that when combined with classical algorithms, accurate mental workload assessment is achieved. To test the proposed algorithm, data from 48 participants was collected as they performed the Revised Multi-Attribute Task Battery-II (MATB-II) under a low and a high workload setting, either while walking/jogging on a treadmill, or using a stationary exercise bicycle. Accuracy as high as 95% could be achieved with a random forest based mental workload classifier with ambulant users. Moreover, an increase in gamma activity was found in the parietal cortex, suggesting a connection between sensorimotor integration, attention, and workload in ambulant users.

Highlights

Many professions, such as first responders and pilots are often faced with cognitive challenges including information overload, multitasking, interruptions, and fatigue
We found that while some improvements were seen relative to using noisy raw data, overall mental workload (MW) measurement performance levels remained lower than what has typically been reported for stationary users
Ablation Study In order to estimate the impact of the adaptive filter on EEG enhancement, mental workload classification accuracy is reported with and without its use

Summary

Introduction

Many professions, such as first responders (firemen, policemen, paramedics) and pilots are often faced with cognitive challenges including information overload, multitasking, interruptions, and fatigue. All these factors increase stress and reduce the efficiency with which this complex set of tasks is performed (Grtner et al, 2019). Mental workload assessment can follow three methods: subjective, behavioral, or instrumental/objective. Behavioral methods, in turn, rely on task performance metrics (e.g., accuracy, response times, error rate) to characterize MW states. It is difficult for subjective and behavioral assessment methods to provide real-time measures of MW,

Objectives

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Results

Conclusion