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 signals’ advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.

A robust finite-time control (FTC) framework using continuous terminal sliding-mode control (SMC) and high-order sliding-mode observer (HOSMO) is discussed to realize the trajectory tracking of flexible-joint robots in this article. Control performances of the robots always suffer from unknown matched and mismatched time-varying disturbances. Traditional SMC exists with a chattering phenomenon and cannot cope with mismatched time-varying disturbances due to its inherent structure property. For this reason, two HOSMOs are devised to estimate the time-varying disturbances on the link and motor side, respectively. Then, by fusing the states and disturbance estimations into a novel terminal sliding-mode surface, a continuous robust FTC scheme is developed. The proposed control strategy can not only handle both matched and mismatched time-varying disturbances but also obtain a finite-time convergence performance. The rigorous finite-time stability analysis of the closed-loop system under the proposed control method is guaranteed. The results are illustrated to verify the effectiveness and robustness of the proposed design approach.

The cross-session and cross-subject classification of motor imagery Electroencephalogram (EEG) signals is challenging. This paper presents a Transfer Learning (TL) method to address the cross-session and cross-subject classification of motor imagery Electroencephalogram (EEG) signals, a tricky procedure in brain-computer interface (BCI). Method: We propose a rotation alignment domain adaptation method with Riemannian mean (RMRA). The method uses covariance matrix to represent data feature, and achieves data alignment by rotating the symmetric positive definite (SPD) matrix in Riemannian space. In this process, our proposed matrix-TCA extends the traditional transfer component analysis (TCA) to a matrix form in order to function in the Riemannian framework. Data labels are not required, so the proposed method is unsupervised. In addition, we simplify the calculation process through Riemannian mean. Results: We have performed both offline and online experiments on multiple motor imagery EEG data sets. Our results show that RMRA improves the cross-session and cross-subject classification accuracy. Conclusion and significance: This paper presents a new approach to cross-domain learning, which achieves desirable results and shows great promise in real-life application of the service robot (intelligent wheelchair).

Motor imagery Electroencephalogram (EEG) signals have been widely used in the field of brain-computer interface (BCI) due to their advantage of non-invasiveness and easy acquisition. However, due to distortions in the temporal and local information of the EEG signal and inter-subject variability, it is very time-consuming to perform a calibration procedure designed in a subject-specific manner, which requires a large number of labeled samples. To this end, we construct EEG brain graph based on EEG electrode distribution and propose a new subdomain adversarial network for learning domain-invariant features to achieve cross-domain classification of motor imagery EEG signals. Specifically, the proposed method constructs a brain graph based on the spatial distribution of electrodes and their functional connections, and builds EEG graph data. The subdomain adversarial network aligns data distributions under the same class domain and improves the classification performance of the target domain using source domain labeled samples. We have conducted extensive experiments on two publicly available datasets to verify the effectiveness of the proposed method. In addition, we have done ablation study and visualization analysis to explain the contribution of each component to cross-domain classification.