Motor imagery-based brain-computer interface (MI-BCI) inefficiency phenomenon is one of the biggest challenges in MI-BCI research. BCI inefficiency subject is defined as the subject who cannot achieve classification accuracy higher than 70% since 70% is considered to be the minimum accuracy for communication by BCI. About 15-30% of the people are MI-BCI inefficiency according to the investigation. Most of the existing studies used common spatial patterns (CSP) to extract motor imagery feature and identify MI-BCI inefficiency subject based on the obtained classification accuracy. We think the MI-BCI performance maybe suppressed because CSP mainly extracts event-related desynchronization (ERD) feature, while the features generated by motor imagery are more than that. In this current work, we screened a total of 12 MI-BCI inefficiency subjects by CSP feature firstly, and recorded the motor imagery EEG data of them. Furthermore, we constructed a task-related brain network by calculating the coherence between EEG channels, the graph-based analysis showed that the node degree and clustering coefficient have intensity differences between left and right hand motor imagery. Finally, the two kinds of features were used to discriminate the two tasks. The results showed that both node degree and clustering coefficient features perform better than CSP, and the feature combination of brain network and CSP achieved higher accuracy than a single feature. In particular, a total of four subjects achieved accuracy higher than 70% by node degree and CSP features fusion. This work demonstrates that the accuracy of the MI-BCI inefficiency subject can be increased by using the brain network feature, but the accuracy gains are not high enough; it is worth to try other types of feature extraction algorithms for the MI-BCI inefficiency subject. INDEX TERMS Motor imagery, brain-computer interface (BCI), BCI inefficiency, network feature, feature extraction.

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The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory. The latest development for a more effective QLM has adopted word embeddings as a kind of global dependency information and integrated the quantum-inspired idea in a neural network architecture. While these quantum-inspired LMs are theoretically more general and also practically effective, they have two major limitations. First, they have not taken into account the interaction among words with multiple meanings, which is common and important in understanding natural language text. Second, the integration of the quantum-inspired LM with the neural network was mainly for effective training of parameters, yet lacking a theoretical foundation accounting for such integration. To address these two issues, in this paper, we propose a Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The QMWF inspired LM can adopt the tensor product to model the aforesaid interaction among words. It also enables us to reveal the inherent necessity of using Convolutional Neural Network (CNN) in QMWF language modeling. Furthermore, our approach delivers a simple algorithm to represent and match text/sentence pairs. Systematic evaluation shows the effectiveness of the proposed QMWF-LM algorithm, in comparison with the state of the art quantum-inspired LMs and a couple of CNN-based methods, on three typical Question Answering (QA) datasets. KEYWORDSLanguage modeling, quantum many-body wave function, convolutional neural network ACM Reference Format:

An independent-wheel-drive electric vehicle has the advantage of better implementation of precise motion and stability control. However, when the vehicle is moving on a road that has complex slopes and various adhesion coefficients and is subjected to the structural limitations of the independent-wheel-drive systems, the driving performance will deteriorate. In order to make full use of the drive torque of every motor to improve the vehicle’s climbing and accelerating abilities, on the basis of the designs of a dual-motor coaxial-coupling independent-wheel-drive system and a sliding-mode controller, a coaxial-coupling traction control system was developed. Simulations on coaxial-coupling traction control for a four-wheel-independent-drive electric vehicle were completed. With the innovative coaxial-coupling equipment, the drive torque can be satisfactorily transferred between the wheels at the two sides of one drive shaft. When one of the driving wheels begins to slip, the torque transmission will increase rapidly, the probability that wheel slipping occurs will be reduced and the vehicle’s driving force can be enhanced. Also, the chatter of the traction control system can be quietened effectively, and the dynamicity and trafficability can be improved. In addition, with the additional yaw moment generated by the torque coupling, the system also has the auxiliary effect of improving the high-velocity lateral stability of the vehicle on a road which has a low adhesion coefficient.