Various spatial filters have been proposed to enhance the target identification performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The current methods only extract the target-related information from the corresponding stimulus to learn the spatial filter parameter. However, the SSVEP data from neighboring stimuli also contain frequency information of the target stimulus, which could be utilized to further improve the target identification performance. In this paper, we propose a new method incorporating SSVEPs from the neighboring stimuli to strengthen the target-related frequency information. First, The spatial filter is obtained by maximizing the summation of covariances of SSVEP data corresponding to the target and its neighboring stimuli. Then the correlation features between spatially filtered templates and test data are calculated for target detection. For the performance evaluation, we implemented the offline experiment using the 40class benchmark dataset from 35 subjects and the 12-target selfcollected dataset from 11 subjects. Compared with the stateof-art spatial filtering methods, the proposed method showed superiority in classification accuracy and information transfer rate (ITR). The comparison results demonstrate the effectiveness of the proposed spatial filter for target identification in SSVEPbased BCIs.

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Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and timevarying properties of surface electromyography (sEMG) signals, resulting in domain shift impacts on DL models. To this end, a reconstruction-based method is proposed for domain shift quantification. Herein, a prevalent hybrid framework that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM), i.e. CNN-LSTM, is selected as the backbone. The paring of auto-encoder (AE) and LSTM, abbreviated as LSTM-AE, is proposed to reconstruct CNN features. Based on reconstruction errors (RErrors) of LSTM-AE, domain shift impacts on CNN-LSTM can be quantified. For a thorough investigation, experiments were conducted in both hand gesture classification and wrist kinematics regression, where sEMG data were both collected in multi-days. Experiment results illustrate that, when the estimation accu-Manuscript

The application of optical heterodyne detection is very demanding, and the roughness of target surface will seriously affect the echo heterodyne detection. In the study of electromagnetic scattering of rough target, Kirchhoff approximation method (KA) is suitable for studying the situation of small rough surface fluctuation. Through studying the laser scattering characteristics of target surface, it can be found that it plays an important role in the extraction of rough target characteristics, detection and acquisition of target echo data. Based on the experimental theory and electromagnetic scattering theory, this paper explores the influence and rule of rough surface on detection, verifies the correctness and feasibility of experimental theory by comparing and analyzing the results. It can be seen from the simulation results of heterodyne echo phase difference that the distortion in front of the detection return wave and the "decoherence" effect of intermediate frequency signal are mainly caused by the root mean square height and correlation length of rough surface. Simulation results based on the two theories show that, with the increase of roughness, the two signal values decrease sharply and then tend to zero; with the increase of correlation length, the two signal values increase first and then tend to be stable. The correctness and feasibility of heterodyne experiment theory are verified by comparing and analyzing the two simulation results.

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