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EEG has been proved to be an effective tool for researchers’ cognition and mental workload by detecting the changes of brain activity potential. The mental workload of pilots in aviation flight is closely related to the characteristics of flight tasks. The previous methods have problems such as lack of objectivity, low EEG analysis ability and lack of real-time analysis ability. In order to solve these problems, this paper proposes a multi-dimensional data fusion brain workload calculation method based on flight effect evaluation, which integrates vision, operational behavior and visual gaze, and classifies and analyzes them in combination with EEG data. This method evaluates the mental workload of pilots from three aspects: visual gaze behavior, control behavior and flight effect in the simulated flight experimental environment, and realizes a more objective mental workload analysis. Then, the synchronously collected EEG data are segmented and sampled to form a dataset, and an LSTM neural network model integrating attention mechanism is established, in which the attention mechanism is used to improve the feature processing ability of the network model for the classification of complex EEG data. After machine learning training, the final model can achieve 94% detection accuracy for 2-s EEG data, and has the ability of real-time analysis in the application environment. Compared with the previous similar LSTM model, the accuracy is improved by 6%, which also shows the effectiveness of the model.

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.

We have applied functional near-infrared spectroscopy (fNIRS) to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex. We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks. To quantify the potential of fNIRS for mental workload discrimination, we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin (HbO₂) and deoxyhemoglobin (HbR) concentration changes associated with the working memory tasks. We have found classification success rates in the range of 44–72%, which are significantly higher than the corresponding chance level (for random data) of 19.1%. This work shows the potential of fNIRS for mental workload classification, especially when more parameters (rather than just the amplitude of concentration changes used here) and more sophisticated classification algorithms (rather than the simple 3-nearest neighbor algorithm used here) are considered and optimized for this application.

Electroencephalography (EEG) has been recognized as a competent mode for cognitivists to study brain activity potential variations with imposed mental workload. The elementary focus of a game design is to provide entertainment to the players and be a source of recreation and relaxation at the same time. However, it is observed that with increasing difficulty levels the game may start imposing a mental workload beyond a critical threshold. To work on such an issue, this paper presents a multi-domain EEG features-based cognitive model that classifies the mental workload levels on a difficulty basis. It investigated the brain activity of gamers while spotting the differences in similar-looking pictures at three levels of difficulty. The collected brain signatures developed a mental workload model using a Support Vector Machine (SVM) within an accuracy range of 84% to 98% ($p < 0.05$). After the machine learning-based modeling, the model was subjectively validated with NASA’s Task Load Index (NASA-TLX) rating scale. Every level was rated by the players for different mental workload factors and a workload score was obtained for every level. A correlation study showed the association of different EEG features with the workload score within a correlation coefficient range of 0.306 to 0.748 ($p < 0.05$-2-tailed). Findings of this type may lead to the scholarly and professional measurement and improvement of cognitive aptitude.

Considering the poor generalization ability of the subject-specific Mental Workload (MWL) classifier, a cross-subject MWL recognition framework was proposed in this paper. In the proposed framework, Kernel Spectral Regression (KSR) and Transferable Discriminative Dimensionality Reduction (TDDR) methods were employed to reduce the feature dimensionality and transfer the classification knowledge from the source domain to the target one. The data analysis results showed that the proposed framework and the related methods can effectively improve the accuracy of MWL recognition.