Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain's intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What's more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG sig-Manuscript

In this paper, couple-group consensus is investigated for a kind of heterogeneous multi-agent systems (HMASs) with Markov switching. Some novel couple-group consensuses have been proposed, in which cooperative-competitive interaction, Markov switching and time delay are all considered. For Markov switching, the transitive rate of probability can be divided into two cases: known or partly known. Based on stochastic delta operator, probability, graph and stability theories, the leader-following and pinning couple-group consensus of this system be converted into analyzing the stability of related switching delta operator system. In the obtained results, some conservative conditions, such as the balance of in or out degree, strong connectivity and containing a spanning tree, can be longer strictly demanded. Some numerical examples are given to show the validity of the acquired results.

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In this article, the couple-group secure consensus of a kind of heterogeneous multi-agent systems (HMASs) with cooperative-competitive relation and time delays has been investigated if it receives malicious attacks. Based on cooperative-competitive relation and time delays, a new couplegroup secure consensus protocol is designed. By applying the graph theory, linear algebra theory, probability theory, the linear combination theory and Getschgorin theorem, several sufficient conditions have been obtained to realize the couple-group secure consensus for this system. The obtained results also show the upper limitation of input delay can be computed if the parameters of system are given. It worth pointing out the topology of HMASs is no more needed to contain a spanning tree or meet with the balance of in or out degree in the obtained results. In additional, an adaptive function is added to speed up the convergence of consensus. Combined these obtained results with T-test, a novel detection algorithm has also been designed to determine that the nodes are security or not. Some examples have been given to prove the effectiveness of these obtained results and the improved secure detection algorithm.

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