Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

Abstract

Background and Objective: Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs). Methods: Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon’s task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis. Results: MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively). Conclusions: The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

Investigating the Relationship between Noise Exposure and Human Cognitive Performance: Attention, Stress, and Mental Workload Based on EEG Signals Using Power Spectrum Density

“…The analysis of the cognitive aspects of mental workload in this study showed that at a noise level of 70 dB with a theta-to-alpha ratio value that increased from the noise level below, participants started mentioning how noise affected their mental workload. The results indicate a correlation between band ratios and mental workload stages, particularly between theta and alpha bands, aligning with the findings of [63,80]. Additionally, a study by [81] also justifies its potential theta-to-alpha ratio as an indicator of workload.…”

“…Different cognitive states correspond to different indicators, some being controversial due to different experimental tasks [35,62]. In this study, after reviewing the relevant literature, three indicators were selected as cognitive performance measurement metrics, namely the cognitive aspects of attention measured based on the prefrontal beta signals of the AF3, AF4, F3, and F4 channels, stress aspects measured based on the alpha lateral frontal cortex signals of the F7 and F8 channels, and the mental workload aspects based on the theta/alpha ratios of the AF3, AF4, F3, F4, F7, F8, FC5, and FC6 channels [35,62,63].…”

“…The cognitive aspect of mental workload is measured based on the theta-to-alpha ratio, which was calculated by the alpha band strength over the frontal channels AF3, AF4, F3, F4, F7, F8, FC5, and FC6 [35,63,68,69]. The calculation of the theta-to-alpha ratio is performed using the mean value of the PSD alpha band and the average PSD value of the theta band [69].…”

Reproducible machine learning research in mental workload classification using EEG

“…Among the relevant thirteen studies, reproducibility was demonstrated for different electrode configurations and preprocessing pipelines (Mastropietro et al, 2023 ), for different settings of 2D and 3D environments (Kakkos et al, 2019 ), for a larger number of participants (Radüntz et al, 2020 ), for different tasks (Parekh et al, 2018 ; Boring et al, 2020 ; Sciaraffa et al, 2022 ), and over time (Gevins et al, 1998 ; Putze et al, 2013 ; Aricò et al, 2015 , 2016b ; Ortiz et al, 2020 ; Fox et al, 2022 ; Roy et al, 2022 ). Gevins et al ( 1998 ) also tested their findings on separate tasks to check cross-task performance and finally on data from a new participant to observe cross-subject performance.…”

Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications

“…The paper “Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines” [ 5 ] evaluates the reproducibility and sensitivity of mental workload assessment from EEG signals using different electrode configurations and pre-processing pipelines. The findings provide valuable insights into developing reliable methods for assessing cognitive tasks, crucial for enhancing human performance in various domains.…”