STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI.

Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals.

Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.

In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails.

EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.

Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper.

Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.

Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL.

EEG multi-domain feature transfer based on sparse regularized Tucker decomposition.

Tensor analysis of electroencephalogram (EEG) can extract the activity information and the potential interaction between different brain regions. However, EEG data varies between subjects, and the existing tensor decomposition algorithms cannot guarantee that the features across subjects are distributed in the same domain, which leads to the non-objectivity of the classification result and analysis, In addition, traditional Tucker decomposition is prone to the explosion of feature dimensions. To solve these problems, combined with the idea of feature transfer, a novel EEG tensor transfer algorithm, Tensor Subspace Learning based on Sparse Regularized Tucker Decomposition (TSL-SRT), is proposed in this paper.

Analysis of Multiscale Corticomuscular Coupling Networks Based on Ordinal Patterns.

The coupled analysis of corticomuscular function based on physiological electrical signals can identify differences in causal relationships between electroencephalogram (EEG) and surface electromyogram (sEMG) in different motor states. The existing methods are mainly devoted to the analysis in the same frequency band, while ignoring the cross-band coupling, which plays an active role in motion control. Considering the inherent multiscale characteristics of physiological signals, a method combining Ordinal Partition Transition Networks (OPTNs) and Multivariate Variational Modal Decomposition (MVMD) was proposed in this paper.

Joint Filter-Band-Combination and Multi-View CNN for Electroencephalogram Decoding.

Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on parameter design to extract features, whereas deep learning algorithms represented by convolution neural network (CNN) can automatically extract features, which is more suitable for BCI applications.

ULFAC-Net: Ultra-Lightweight Fully Asymmetric Convolutional Network for Skin Lesion Segmentation.

Segmentation of skin lesions is a critical step in the process of skin lesion diagnosis. Such segmentation is challenging due to the irregular shape, fuzzy contours and severe noise interference in the skin lesion region. Existing deep learning-based skin lesion segmentation methods are usually computationally expensive, hindering their deployment in dermoscopic devices with poor computational power.

Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition.

Tensor analysis can comprehensively retain multidomain characteristics, which has been employed in EEG studies. However, existing EEG tensor has large dimension, making it difficult to extract features. Traditional Tucker decomposition and Canonical Polyadic decomposition(CP) decomposition algorithms have problems of low computational efficiency and weak capability to extract features.

Double Stage Transfer Learning for Brain-Computer Interfaces.

In the application of brain-computer interfaces (BCIs), electroencephalogram (EEG) signals are difficult to collect in large quantities due to the non-stationary nature and long calibration time required. Transfer learning (TL), which transfers knowledge learned from existing subjects to new subjects, can be applied to solve this problem. Some existing EEG-based TL algorithms cannot achieve good results because they only extract partial features.

Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification.

Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in brain-computer interface (BCI) technology have facilitated the detection of MI from electroencephalogram (EEG). Previous studies have proposed various EEG-based classification algorithms to identify the MI, however, the performance of prior models was limited due to the cross-subject heterogeneity in EEG data and the shortage of EEG data for training.

Self-Supervised EEG Emotion Recognition Models Based on CNN.

Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on emotion classification show resource utilization because they use the fully-supervised learning methods.

TwinsReID: Person re-identification based on twins transformer's multi-level features.

In the traditional person re-identification model, the CNN network is usually used for feature extraction. When converting the feature map into a feature vector, a large number of convolution operations are used to reduce the size of the feature map. In CNN, since the receptive field of the latter layer is obtained by convolution operation on the feature map of the previous layer, the size of this local receptive field is limited, and the computational cost is large.

Domain Adaptive Algorithm Based on Multi-Manifold Embedded Distributed Alignment for Brain-Computer Interfaces.

The use of transfer learning in brain-computer interfaces (BCIs) has potential applications. As electroencephalogram (EEG) signals vary among different paradigms and subjects, existing EEG transfer learning algorithms mainly focus on the alignment of the original space. They may not discover hidden details owing to the low-dimensional structure of EEG.

Classification of Working Memory Loads via Assessing Broken Detailed Balance of EEG-FNIRS Neurovascular Coupling Measures.

Human brain breaks the detailed balance to drive a variety of cognitive functions, such as memory. Recently, a promising classification framework of working memory loads has been proposed based on functional magnetic resonance imaging (fMRI) data with relative entropy (RE) measurement to quantify the broken detailed balance of human brain. However, there are limitations in previousely developed methods.

EEG emotion recognition based on enhanced SPD matrix and manifold dimensionality reduction.

Recently, Riemannian geometry-based pattern recognition has been widely employed to brain computer interface (BCI) researches, providing new idea for emotion recognition based on electroencephalogram (EEG) signals. Although the symmetric positive definite (SPD) matrix manifold constructed from the traditional covariance matrix contains large amount of spatial information, these methods do not perform well to classify and recognize emotions, and the high dimensionality problem still unsolved. Therefore, this paper proposes a new strategy for EEG emotion recognition utilizing Riemannian geometry with the aim of achieving better classification performance.

Inter-muscular coherence and functional coordination in the human upper extremity after stroke.

Muscle coordination and motor function of stroke patients are weakened by stroke-related motor impairments. Our earlier studies have determined alterations in inter-muscular coordination patterns (muscle synergies). However, the functional connectivity of these synergistically paired or unpaired muscles is still unclear in stroke patients.

Epilepsy EEG classification method based on supervised locality preserving canonical correlation analysis.

Existing epileptic seizure automatic detection systems are often troubled by high-dimensional electroencephalogram (EEG) features. High-dimensional features will not only bring redundant information and noise, but also reduce the response speed of the system. In order to solve this problem, supervised locality preserving canonical correlation analysis (SLPCCA), which can effectively use both sample category information and nonlinear relationships between features, is introduced.

A novel dynamic brain network in arousal for brain states and emotion analysis.

Background: Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals. New Method: Time factors is integrated to construct a dynamic brain network under high and low arousal conditions.

[Multiple-scale intermuscular coupling network analysis].

In order to more accurately and effectively understand the intermuscular coupling of different temporal and spatial levels from the perspective of complex networks, a new multi-scale intermuscular coupling network analysis method was proposed in this paper. The multivariate variational modal decomposition (MVMD) and Copula mutual information (Copula MI) were combined to construct an intermuscular coupling network model based on MVMD-Copula MI, and the characteristics of intermuscular coupling of multiple muscles of upper limbs in different time-frequency scales during reaching exercise in healthy subjects were analyzed by using the network parameters such as node strength and clustering coefficient. The experimental results showed that there are obvious differences in the characteristics of intermuscular coupling in the six time-frequency scales.

Brain controllability distinctiveness between depression and cognitive impairment.

Alzheimer's disease (AD) is a progressive form of dementia marked by cognitive and memory deficits, estimated to affect ∼5.7 million Americans and account for ∼$277 billion in medical costs in 2018. Depression is one of the most common neuropsychiatric disorders that accompanies AD, appearing in up to 50% of patients.

Sparse representation-based classification with two-dimensional dictionary optimization for motor imagery EEG pattern recognition.

Background: Sparse representation-based classification (SRC) has more advantages in motor imagery EEG pattern recognition, and the quality of dictionary construction directly determines the performance of SRC. In this paper, we proposed a two-dimensional dictionary optimization (TDDO) method to directly improve the performance of SRC.

New Method: Firstly, an initial dictionary was constructed with multi-band features extracted by filter band common spatial pattern (FBCSP).

Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification.

Common spatial pattern (CSP) as a spatial filtering method has been most widely applied to electroencephalogram (EEG) feature extraction to classify motor imagery (MI) in brain-computer interface (BCI) applications. The effectiveness of CSP is determined by the quality of interception in a specific time window and frequency band. Although numerous algorithms have been designed to optimize CSP by splitting the EEG data with a sliding time window and dividing the frequency bands with a set of band-pass filters, simultaneously.

Interactions Between Aging and Alzheimer's Disease on Structural Brain Networks.

Normative aging and Alzheimer's disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks.

Bone age assessment based on deep convolution neural network incorporated with segmentation.

Purpose: Bone age assessment is not only an important means of assessing maturity of adolescents, but also plays an indispensable role in the fields of orthodontics, kinematics, pediatrics, forensic science, etc. Most studies, however, do not take into account the impact of background noise on the results of the assessment. In order to obtain accurate bone age, this paper presents an automatic assessment method, for bone age based on deep convolutional neural networks.