Estimating cognitive workload using a commercial in-ear EEG headset

Abstract

Objective: this study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN 'Guardian'. Approach. Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance of γ band activity in workload estimations. Therefore, each signal was analyzed across low frequency (1–35 Hz) and high frequency (1–100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined.

Main results: workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequency γ band features can improve workload estimation.

Significance: the application of EEG-based Brain–Computer Interfaces beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.

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Identifiers

ORCID for Christoph Tremmel: orcid.org/0000-0002-0324-6626

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