Objective. Estimation of mental workload (MWL) levels by electroencephalography (EEG)-based mental state monitoring systems has been widely explored. Using event-related potentials (ERPs), elicited by ignored auditory probes, minimizes intrusiveness and has shown high performance for estimating MWL level when tested in laboratory situations. However, when facing real-world applications, the characteristics of ERP waveforms, like latency and amplitude, are often affected by noise, which leads to a decrease in classification performance. One approach to mitigating this is using spatial covariance, which is less sensitive to latency and amplitude distortion. In this study, we used ignored auditory probes in a single-stimulus paradigm and tested Riemannian processed covariance-based features for MWL level estimation in a realistic flight-control task. Approach. We recorded EEG data with an eight-channel system from participants while they performed a simulated drone-control task and manipulated MWL levels (high and low) by task difficulty. We compared support vector machine classification performance based on frequency band power features versus features generated via the Riemannian log map operator from spatial covariance matrices. We also compared accuracy of using data segmented as auditory ERPs versus non-ERPs, for which data windows did not overlap with the ERPs. Main results. Classification accuracy of both types of features showed no significant difference between ERPs and non-ERPs. When we ignore auditory stimuli to perform continuous decoding, covariance-based features in the gamma band had area under the receiver-operating-characteristic curve (AUC) of 0.883, which was significantly higher than band power features (AUC = 0.749). Significance. This study demonstrates that Riemannian-processed covariance features are viable for MWL classification under a realistic experimental scenario.

客观的。通过基于脑电图 (EEG) 的精神状态监测系统估计精神负荷 (MWL) 水平已被广泛探索。使用事件相关电位 (ERP)，由忽略的听觉探针引起，可最大限度地减少干扰，并在实验室环境中测试时显示出估计 MWL 水平的高性能。然而，当面对现实世界的应用时，ERP波形的特征，如延迟和幅度，往往会受到噪声的影响，从而导致分类性能下降。缓解这种情况的一种方法是使用空间协方差，它对延迟和幅度失真不太敏感。在这项研究中，我们在单刺激范式中使用了忽略的听觉探针，并在现实的飞行控制任务中测试了基于黎曼处理的协方差特征，用于 MWL 水平估计。方法。我们使用八通道系统记录参与者的 EEG 数据，同时他们执行模拟无人机控制任务并根据任务难度操纵 MWL 水平（高和低）。我们比较了基于频带功率特征的支持向量机分类性能与通过黎曼对数映射算子从空间协方差矩阵生成的特征。我们还比较了使用分段为听觉 ERP 和非 ERP 的数据的准确性，其中数据窗口与 ERP 不重叠。主要结果。两种类型特征的分类准确性在 ERP 和非 ERP 之间没有显着差异。当我们忽略听觉刺激进行连续解码时，伽马波段中基于协方差的特征的接收器操作特性曲线（AUC）下面积为 0.883，明显高于波段功率特征（AUC = 0.749）。意义。这项研究表明，黎曼处理的协方差特征对于现实实验场景下的 MWL 分类是可行的。