Attention model of EEG signals based on reinforcement learning.

Background: Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.

Methods: The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales.

Enhancing EEG and sEMG Fusion Decoding Using a Multi-Scale Parallel Convolutional Network With Attention Mechanism.

Electroencephalography (EEG) and surface electromyography (sEMG) have been widely used in the rehabilitation training of motor function. However, EEG signals have poor user adaptability and low classification accuracy in practical applications, and sEMG signals are susceptible to abnormalities such as muscle fatigue and weakness, resulting in reduced stability. To improve the accuracy and stability of interactive training recognition systems, we propose a novel approach called the Attention Mechanism-based Multi-Scale Parallel Convolutional Network (AM-PCNet) for recognizing and decoding fused EEG and sEMG signals.

Dynamic pruning group equivariant network for motor imagery EEG recognition.

The decoding of the motor imaging electroencephalogram (MI-EEG) is the most critical part of the brain-computer interface (BCI) system. However, the inherent complexity of EEG signals makes it challenging to analyze and model them. In order to effectively extract and classify the features of EEG signals, a classification algorithm of motor imagery EEG signals based on dynamic pruning equal-variant group convolutional network is proposed.

Motor Imagery EEG Decoding Based on Multi-scale Hybrid Networks and Feature Enhancement.

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.

Improved Stretchable and Sensitive Fe Nanowire-Based Strain Sensor by Optimizing Areal Density of Nanowire Network.

Flexible strain sensors, when considering high sensitivity and a large strain range, have become a key requirement for current robotic applications. However, it is still a thorny issue to take both factors into consideration at the same time. Here, we report a sandwich-structured strain sensor based on Fe nanowires (Fe NWs) that has a high GF (37-53) while taking into account a large strain range (15-57.

FB-EEGNet: A fusion neural network across multi-stimulus for SSVEP target detection.

Background: Steady-state visual evoked potential (SSVEP) is a prevalent paradigm of brain-computer interface (BCI). Recently, deep neural networks (DNNs) have been employed for SSVEP target recognition. However, current DNN models can not fully extract information from SSVEP harmonic components, and ignore the influence of non-target stimuli.

Construction of a Competitive Endogenous RNA Network Related to Exosomes in Diabetic Retinopathy.

Background: The competing endogenous RNA (ceRNA) network plays an important role in the occurrence and development of a variety of diseases. This study aimed to construct a ceRNA network related to exosomes in diabetic retinopathy (DR).

Methods: We explored the Gene Expression Omnibus (GEO) database and then analyzed the RNAs of samples to obtain differentially expressed lncRNAs (DELs), miRNAs (DEMs) and mRNAs (DEGs) alongside the progress of DR.

Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary.

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information.

Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition.

Most existing multi-focus color image fusion methods based on multi-scale decomposition consider three color components separately during fusion, which leads to inherent color structures change, and causes tonal distortion and blur in the fusion results. In order to address these problems, a novel fusion algorithm based on the quaternion multi-scale singular value decomposition (QMSVD) is proposed in this paper. First, the multi-focus color images, which represented by quaternion, to be fused is decomposed by multichannel QMSVD, and the low-frequency sub-image represented by one channel and high-frequency sub-image represented by multiple channels are obtained.

Introducing frequency representation into convolution neural networks for medical image segmentation via twin-Kernel Fourier convolution.

Background And Objective: For medical image segmentation, deep learning-based methods have achieved state-of-the-art performance. However, the powerful spectral representation in the field of image processing is rarely considered in these models.

Methods: In this work, we propose to introduce frequency representation into convolution neural networks (CNNs) and design a novel model, tKFC-Net, to combine powerful feature representation in both frequency and spatial domains.

Continuous Terminal Sliding-Mode Control for FJR Subject to Matched/Mismatched Disturbances.

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.

Motor imagery EEG recognition with KNN-based smooth auto-encoder.

As new human-computer interaction technology, brain-computer interface has been widely used in various fields of life. The study of EEG signals cannot only improve people's awareness of the brain, but also establish new ways for the brain to communicate with the outside world. This paper takes the motion imaging EEG signal as the research object and proposes an innovative semi-supervised model called KNN-based smooth auto-encoder (k-SAE).

Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition.

Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence.