Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.This article studies the event-triggered impulsive control (ETIC) with constraints for the stabilization of switched stochastic systems (SSSs). An ETIC scheme with constraints is proposed for SSS by designing two levels of events via three indices 1) a threshold value; 2) a control-free index; and 3) a check period. It is also constrained via a constraint index. Based on the activation probabilities and transition probabilities of subsystems, the stabilizations in terms of the pth moment exponential stability and almost exponential stability are achieved, respectively, by the ETIC with constraints. Moreover, based on the scheme of ETIC with constraints, sampling-based ETIC and random ETIC are proposed, respectively. The stabilization conditions via sampling-based ETIC and random ETIC are also derived. It is shown that the ETIC with constraints is non-Zeno and robust with respect to time delays and can achieve lower impulse frequency than the classic time-based impulsive control and recent ETIC schemes. Finally, two examples are presented to demonstrate the effectiveness of the ETIC with constraints.In this article, probabilistic hesitant fuzzy linguistic preference relations (PHFLPRs) are proposed to present the qualitative pairwise preference information of decision makers (DMs) with hesitation and probability uncertainty assessments. The measurements and improvements of additive consistency and consensus of PHFLPRs are investigated in group decision making (GDM). First, a new concept of probabilistic hesitant fuzzy linguistic term sets is defined. Second, the consistency and consensus measurements are established to survey the additive consistency and consensus levels of PHFLPRs. Subsequently, an optimization model is developed to improve the unacceptably additive consistent PHFLPR. By optimizing the unacceptable consensual PHFLPRs with repeating additive consistency improvement, the acceptably additive consistent and consensual PHFLPRs are obtained, based on which DMs' weights are determined objectively and then, the collective PHFLPR is aggregated from individual PHFLPRs. GSK503 in vivo Alternatives' priority weights are derived from the collective PHFLPR as GDM. Finally, an example about failure criticality analysis is given, and a comparison analysis is presented.This note studies an enclosing control problem for a multiagent system with a moving target of unknown bounded velocity. The objectives are to let each agent move along a circular orbit with a prescribed radius centered at the target and maintain desired spacing from neighboring agents. A distributed controller composed of three parts is designed by only using the relative position information from each agent to the target and its neighbors. The first two parts are designed to achieve target circling and spacing adjustment, respectively. The last part is designed discontinuously to compensate for the unknown bounded velocity of the target. Due to the discontinuously distributed controller, sufficient conditions are given by a nonsmooth analysis. Furthermore, the agents are shown to have order preservation and collision avoidance properties when the target is stationary. The effectiveness of theoretical results is illustrated by simulations.Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced technique for seizure prediction. Recent deep learning approaches, which fail to fully explore both the characterizations in EEGs themselves and correlations among different electrodes simultaneously, generally neglect the spatial or temporal dependencies in an epileptic brain and, thus, produce suboptimal seizure prediction performance consequently. To tackle this issue, in this article, a patient-specific EEG seizure predictor is proposed by using a novel spatio-temporal-spectral hierarchical graph convolutional network with an active preictal interval learning scheme (STS-HGCN-AL). Specifically, since the epileptic activities in different brain regions may be of different frequencies, the proposed STS-HGCN-AL framework first infers a hierarchical graph to concurrently characterize an epileptic cortex under different rhythms, whose temporal dependencies and spatial couplings are extracted by a spectral-temporal convolutional neural network and a variant self-gating mechanism, respectively.