Mental workload (MW) assessment is crucial for understanding human mental state. Cross-subject MW analysis based on electroencephalogram (EEG) signals is an important way. In this paper, a fine-grained and multi-scale motif (FGMSM) features extraction method is proposed, and the proposed features together with original EEG data are used as the input of bidirectional long short-term memory (Bi-LSTM) to evaluate the cross-subject mental workload. First, the EEG signal of each channel is decomposed based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm. Second, for the motif structure consisting of three nodes, multi-scale detection is carried out in each intrinsic mode function, and the proportion of each motif structure is extracted as the newly extracted features. Then, the statistical differences of the extracted features between different MW levels are analyzed by using the t-test, and the features with statistical differences are selected for the cross-subject MW assessment. Finally, based on the public dataset with 26 subjects, Bi-LSTM and a variety of machine learning algorithms are used to classify the levels of cross-subject MW. The results show that the Bi-LSTM classification method with the original EEG data and the proposed features show the most positive results. Therefore, the FGMSM features proposed in this paper with Bi-LSTM provide a new technique for the assessment of cross-subject MW based on EEG signals.

中文翻译：

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使用 BI-LSTM 进行跨学科心理工作量评估的细粒度和多尺度主题特征

心理工作量 (MW) 评估对于了解人类心理状态至关重要。基于脑电图（EEG）信号的跨学科MW分析是一种重要的方法。在本文中，提出了一种细粒度和多尺度主题（FGMSM）特征提取方法，并将提出的特征与原始脑电图数据一起作为双向长短期记忆（Bi-LSTM）的输入来评估跨学科的心理工作量。首先，基于改进的自适应噪声完全集成经验模态分解（ICEEMDAN）算法对每个通道的脑电信号进行分解。其次，对于由三个节点组成的motif结构，在每个固有模态函数中进行多尺度检测，提取每个motif结构的比例作为新提取的特征。然后，吨-test，并选择具有统计差异的特征进行跨学科 MW 评估。最后，基于包含 26 个学科的公共数据集，使用 Bi-LSTM 和多种机器学习算法对跨学科 MW 的级别进行分类。结果表明，具有原始 EEG 数据和所提出特征的 Bi-LSTM 分类方法显示出最积极的结果。因此，本文提出的具有 Bi-LSTM 的 FGMSM 特征为基于 EEG 信号的跨主题 MW 评估提供了一种新技术。