Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload conditions. Because brain dynamics are complex across different tasks, it is difficult to propose efficient human-designed features based on prior knowledge. Therefore, this study proposes a concatenated structure of deep recurrent and 3D convolutional neural networks (R3DCNN) to learn EEG features across different tasks without prior knowledge. First, this study adds frequency and time dimensions to EEG topographic maps based on a Morlet wavelet transformation. Then, R3DCNN is proposed to simultaneously learn EEG features from the spatial, spectral and temporal dimensions. The proposed model is validated based on EEG signals collected from twenty subjects. This study employs a binary classification of low and high mental workload across spatial n-back and arithmetic tasks. The results show that the R3DCNN achieves an average accuracy of 88.9%, which is a significant increase compared with that of the state-of-the-art methods. In addition, the visualization of the convolutional layers demonstrates that the deep neural network can extract detailed features. These results indicate that R3DCNN is capable of identifying the mental workload levels for cross-task conditions.

People’s mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its temporal features have proven to be useful in addressing this problem. The fusion of the two types of features can provide rich distinguishing information for improving mental workload assessment. Benefiting from the progress of deep learning, this study proposes the two-stream neural networks (TSNN) for fusing the two types of EEG features. Compared with hand-crafted features, the TSNN can learn and fuse EEG features from the spectral and temporal dimensions automatically without prior knowledge. The TSNN includes a spectral stream and a temporal stream. Each stream consists of a convolutional neural network (CNN) and a temporal convolutional network (TCN) to learn spectral or temporal features from EEG topographic maps. To fuse the learned spectral and temporal information, we concatenate the output of the two streams prior to the fully connected layer. EEG data were collected from 17 subjects who performed n-back tasks with easy, medium and hard difficulty levels, leading to a three-class mental workload classification. The results show that the TSNN achieves an average accuracy of 91.9%, which is a significant improvement over baseline classifiers based on hand-crafted features. The TSNN also outperforms state-of-the-art deep learning methods developed for EEG classification. The results indicate that the proposed structure is promising for fusing spectral and temporal features for mental workload assessment. In addition, it provides a high-precision approach for potential applications during cognitive activities.

The active contour models with level set evolution have been visited with a vast number of methods for image segmentation. They can be mainly classified into region-based and edge-based models, and it has been validated that the hybrid variants combining both region and edge information can improve the segmentation performance. However, to the best of our knowledge, the theoretical foundation of collaboration mechanism between the region and edge information is limited. Specifically, most existing hybrid models just combining all the energy terms together, resulting in great challenges of choosing an appropriate weight coefficient for each term and accommodating different modalities of imaging. To overcome these difficulties, this paper proposes a region and edge synergetic level set framework, named RESLS. It provides an approach to construct new hybrid level set models using a normalized intensity indicator function that allows the region information easily embedding into the edge-based model. In this case, the energy weights of region and edge terms can be constrained by the global optimization condition deduced from the framework. Some representative as well as state-of-the-art models are taken as examples to demonstrate the generality of our method. The experiments validate that under the guidance of the optimization condition, the weighting parameter of each term can be reliably chosen. Meanwhile, the segmentation accuracy, robustness and computational efficiency of RESLS can be improved compared with its component models.

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.

Emotion recognition is a process in which emotions are identified and recognized according to emotion-related bio signals. Wearable biosensor network expands the application of emotion recognition by measuring different emotion-related bio signals with wearable and portable hardware structures to meet specific needs in complicated measuring environment. This paper develops a multimodal emotion recognition method in wearable biosensor network. Reputation-driven Support Vector Machine (RSVM) classification algorithm is proposed to reduce the classification error caused by wearable sensor nodes. Reputations are figured out by similarity evaluation based on correlation calculation and are used for training sample selection and fuzzy membership degree determination. The experiment results indicate that this framework realizes reliable emotion recognition in wearable web-enable sensing environment and provides a solution to primary recognition and monitoring of emotional states and spiritual health.