Electroencephalography (EEG) offers non-invasive, real-time mental workload assessment, which is crucial in high-stakes domains like aviation and medicine and for advancing brain-computer interface (BCI) technologies. This study introduces a customized ConvNeXt architecture, a powerful convolutional neural network, specifically adapted for EEG analysis. ConvNeXt addresses traditional EEG challenges like high dimensionality, noise, and variability, enhancing the precision of mental workload classification. Using the STEW dataset, the proposed ConvNeXt model is evaluated alongside SVM, EEGNet, and TSception on binary (No vs SIMKAP task) and ternary (SIMKAP multitask) class mental workload tasks. Results demonstrated that ConvNeXt significantly outperformed the other models, achieving accuracies of 95.76% for binary and 95.11% for multi-class classification. This demonstrates ConvNeXt's resilience and efficiency for EEG data analysis, establishing new standards for mental workload evaluation. These findings represent a considerable advancement in EEG-based mental workload estimation, laying the foundation for future improvements in cognitive state measurements. This has broad implications for safety, efficiency, and user experience across various scenarios. Integrating powerful neural networks such as ConvNeXt is a critical step forward in non-invasive cognitive monitoring.

翻译：脑电图（EEG）提供了非侵入式、实时的心理负荷评估，这对于航空和医疗等高风险领域以及推进脑机接口（BCI）技术至关重要。本研究引入了一种定制的ConvNeXt架构，这是一种强大的卷积神经网络，专门适用于EEG分析。ConvNeXt解决了传统EEG分析中的高维度、噪声和变异性等挑战，从而提升了心理负荷分类的精度。使用STEW数据集，将所提出的ConvNeXt模型与SVM、EEGNet和TSception在二元（无任务 vs SIMKAP任务）和三元（SIMKAP多任务）心理负荷分类任务上进行了评估。结果表明，ConvNeXt显著优于其他模型，在二元分类和多元分类中分别达到了95.76%和95.11%的准确率。这证明了ConvNeXt在EEG数据分析中的鲁棒性和高效性，为心理负荷评估设立了新标准。这些发现代表了基于EEG的心理负荷估计领域的重大进展，为未来认知状态测量的改进奠定了基础。这对多种场景下的安全性、效率和用户体验具有广泛意义。集成如ConvNeXt等强大的神经网络，是非侵入式认知监测向前迈出的关键一步。