We develop an online learning algorithm for a navigation platform to route travelers in a congested network with multiple origin-destination (o-d) pairs while simultaneously learning unknown cost functions of road segments (edges) using the crowd-sourced data. The number of travel requests is randomly realized, and the travel time of each edge is stochastically distributed with the mean being a linear function that increases with the edge load (the number of travelers who take the edge). In each step of our algorithm, the platform updates the estimates of cost function parameters using the collected travel time data, and maintains a rectangular confidence interval of each parameter. The platform then routes travelers in the next step using an optimistic strategy based on the lower bound of the up-to-date confidence interval. The key aspects of our setting include (i) the size and the spatial distribution of collected travel time data depend on travelers’ routing strategies; (ii) we evaluate the regret of our algorithm for platforms with different objectives, ranging from minimizing the social cost to minimizing the individual cost of self-interested users. We prove that the regret upper bound of our algorithm is $O(\sqrt{T}\log(T)|E|)$, where $T$ is the time horizon, and $|E|$ is the number of edges in the network. Furthermore, we show that the regret bound decreases as the number of travelers increases, which implies that the platform learns faster with a larger user population. Finally, we implement our algorithm on the network of New York City, and demonstrate the efficacy of the proposed algorithm.

Electroencephalogram (EEG) has been widely used for the detection of anxiety because of its ability to reflect the functional activities of the brain. However, EEG alone may not provide precision in the detection of anxiety because other emotional disorders usually trigger the same changes in brain function. To discover effective diagnostic indicators and to achieve more precise anxiety detection, we integrate eye movement information into EEG and divide the features into groups according to their respective characteristics. Then, we use group sparse canonical correlation analysis (GSCCA) to investigate group structure information among EEG and eye movement features and obtain an effective fusion representation of EEG and eye movement to achieve more precise detection of anxiety mood. The experimental results from 45 anxious subjects and 47 normal controls from the Healthy Brain Network (HBN) dataset showed that GSCCA could be effectively used to explore the correlation between EEG features within different scalp regions and eye movement features from several aspects. Visual behaviors, including saccades and fixation, are more linearly related to the power spectrum of EEG on the scalp area corresponding to the visual region of the brain. The ultimate fusion representation achieved an optimal classification accuracy of 82.70 percent with the support vector machine (SVM) classifier on the gamma band of EEG.

Driver fatigue has become a leading cause of accidents and death in the transportation industry. Electroencephalography (EEG)-based fatigue detection can be a good way to reduce accidents and improve safety and efficiencies throughout the transportation system. In this article, we focus on investigating whether the spatial–temporal changes in the relations between EEG channels are specific to different driving states. EEG signals were first partitioned into several segments, and the covariance matrices obtained from each segment were input into a recurrent neural network to extract high-level temporal features. Meanwhile, the covariance matrices of whole signals were leveraged to extract spatial characteristics that were fused with temporal features to obtain comprehensive spatial–temporal information. In experiments on an open benchmark dataset, our method achieved an excellent classification accuracy of 89.28% and showed superior performance compared to several other state-of-the-art methods. These results indicate that our method can enable higher performance in driver fatigue detection.

Depression is a mental disorder with emotional and cognitive dysfunction. The main clinical characteristic of depression is significant and persistent low mood. As reported, depression is a leading cause of disability worldwide. Moreover, the rate of recognition and treatment for depression is low. Therefore, the detection and treatment of depression are urgent. Multichannel electroencephalogram (EEG) signals, which reflect the working status of the human brain, can be used to develop an objective and promising tool for augmenting the clinical effects in the diagnosis and detection of depression. However, when a large number of EEG channels are acquired, the information redundancy and computational complexity of the EEG signals increase; thus, effective channel selection algorithms are required not only for machine learning feasibility, but also for practicality in clinical depression detection. Consequently, we propose an optimal channel selection method for EEG-based depression detection via kernel-target alignment (KTA) to effectively resolve the abovementioned issues. In this method, we consider a modified version KTA that can measure the similarity between the kernel matrix for channel selection and the target matrix as an objective function and optimize the objective function by a proposed optimal channel selection strategy. Experimental results on two EEG datasets show that channel selection can effectively increase the classification performance and that even if we rely only on a small subset of channels, the results are still acceptable. The selected channels are in line with the expected latent cortical activity patterns in depression detection. Moreover, the experimental results demonstrate that our method outperforms the state-of-the-art channel selection approaches.

The World Health organization (WHO) stated that the concept of health has been widened to subjectively experienced dimensions such as fatigue and chronic fatigue syndrome (CFS). With the increasing pressure of the current life, persistent fatigue caused by sustained high-pressure work will not only be hazardous to health, but also give rise to unexpected consequences. In particularly, fatigue driving induced by long time driving has become a leading cause of accidents and death in the transportation. In this study, we investigate electroencephalography(EEG)-based fatigue detection of drivers through the spatial-temporal changes in the relations between EEG channels. EEG signals are firstly partitioned into several segments and the covariance matrices obtained from each segment are fed into a recurrent neural network to extract high-level temporal features. Then, the covariance matrices of whole signals are leveraged to extract spatial characteristics, which will be fused with temporal features to obtain comprehensive spatial-temporal information. Experimental results on a benchmark dataset showed that our method obtained an optimal classification accuracy of 91.042% and outperformed some state-of-the-art methods. These results indicate that our method is reliable and feasible for fatigue detection, which also provides a novel solution for EEG modeling.