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近年来,脑力负荷估计已经经历了广泛的研究,因为监测认知负荷的能力能够防止认知超负荷并且改善工作场所安全.脑电图(EEG)信号已经被发现是一种客观和非侵入性的脑力负荷的测量方式.然而,作为实时脑力负荷监测和脑机接口研究的重要一步,基于单试验EEG数据的认知负荷的评估一直是一个重大的挑战.最近,许多高级的特征提取方法和机器学习算法已经被采用于基于EEG的脑力负荷评估中.在本研究中,使用在具有2个难度水平的n-back任务的执行期间记录的EEG数据进行了单试验脑力负荷分类,测试了3种类型的特征提取的有效性(谱功率、离散小波变换和公共空间滤波),并评估了4种分类算法的性能(支持向量机、K-近邻、随机森林和梯度推进分类器).研究结果表明,公共空间滤波是性能最好的基于单试验的脑力负荷分类的特征提取方法,而且最佳性能可以通过将来自谱功率或离散小波变换的特征与来自公共空间滤波的特征相结合,并采用随机森林分类器来实现.这项研究可能对基于单试验脑电图数据的脑力负荷评估中的特征提取方法以及机器学习算法的选择提供一些有用的指导.“,”Mental workload estimation has been under extensive investigation over the years,because the capability of monitoring the cognitive workload enables the prevention of cognitive overloading and improvement of workplace safety.Electroencephalogram (EEG) signals has been found to be an objective and non-intrusive measure of mental workload.However,the evaluation of cognitive workload based on single-trial EEG data,which is an essential step towards real-time workload monitoring and brain-computer interface,has been a major challenge.Recently,a number of advanced feature extraction methods and machine learning algorithms have been employed in EEG-based mental workload assessment.In this study,we performed single-trial workload classification using the EEG data recorded during the performance of n-back tasks with 2 levels of difficulty (corresponding to low and high levels of workload respectively),examined the effectiveness of 3 types of feature extraction (spectral power,discrete wavelet transform and common spatial filtering),and evaluated the performance of 4 classification algorithms (support vector machine,K-nearest neighbors,random forest and gradient boosting classifiers).Our findings indicate that common spatial filtering was the best-performing individual feature extraction method for single-trial-based workload classification,and the optimal performance was achieved by combining the features from either spectral power or discrete wavelet transform with those from common spatial filtering,and adopting the random forest classifier.This study might provide some helpful guidance on the selection of feature extraction methods as well as machine learning algorithms in mental workload evaluation based on single-trial EEG data.