使用商用耳内脑电图耳机估算认知工作量。

Christoph Tremmel, Dean J Krusienski, M C Schraefel
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引用次数: 0

摘要

研究目的本研究使用商用耳内脑电图(EEG)系统 IDUN "Guardian",对在两项不同任务中估计各种脑力劳动负荷水平的潜力进行了调查:受试者完成了两项经典的脑力劳动任务:n-back 任务和心算任务。在完成这些任务的过程中,同时收集耳内和传统脑电图数据。为了便于进行更全面的比较,我们有意提高了任务的复杂性,使其超出了一般水平。我们还特别强调要了解伽玛波段活动在工作量估算中的重要性。因此,对每个信号都进行了低频(1-35 赫兹)和高频(1-100 赫兹)范围的分析。此外,还从常规脑电图记录中提取并检查了替代耳内脑电图测量值:使用耳内脑电图估算工作量的结果具有显著的统计学意义,在 n-back 任务中,四个等级的工作量估算率为 44.1%,两个等级的工作量估算率为 68.4%,超过了偶然水平。与耳内脑电图相比,传统脑电图的性能明显更高,在相应任务中分别达到了 80.3% 和 92.9% 的准确率,错误率也低于天真预测器。所开发的替代测量方法取得了更好的结果,准确率分别达到 57.5% 和 85.5%,从而为增强未来的耳内式系统提供了启示。值得注意的是,大多数高频范围的信号在准确性方面优于低频范围的信号,这验证了高频伽玛频段特征可以改善工作量估算:基于脑电图的脑机接口(BCI)在实验室以外的应用往往受到实际限制的阻碍。入耳式脑电图系统为这一问题提供了一个很有前景的解决方案,有可能实现日常使用。本研究评估了商用入耳式耳机的性能,并提供了提高效率的指导原则。
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Estimating cognitive workload using a commercial in-ear EEG headset.

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 gamma 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 gamma band features can improve workload estimation. Significance: The application of EEG-based Brain-Computer Interfaces (BCIs) 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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