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. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.

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

Objective.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.Approach.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. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine.Main result.The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.Significance.The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.

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. In TSL-SRT, new EEG samples are considered as the target domain and original samples as the source domain. The target features can be obtained by projecting the target tensor to the source feature space to ensure that all features are in the same domain. Furthermore, to solve the problem of dimension explosion caused by TSL-SRT, a redundant EEG features screening algorithm is adopted to eliminate the redundant features, and achieves 77.8%, 73.2% and 75.3% accuracy on three BCI datasets. By visualizing the spatial basic matrix of the feature space, it can be seen that TSL-SRT is effective in extracting the features of active brain regions in the BCI task and it can extract the multi-domain features of different subjects in the same domain simultaneously, which provides a new method for the tensor analysis of EEG.

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. The EEG and sEMG were firstly decomposed on a time-frequency scale using MVMD, and then the coupling strength was calculated by the OPTNs to construct a corticomuscular coupling network, which was analyzed with complex network parameters. Experimental data were obtained from a self-acquired dataset consisting of EEG and sEMG of 16 healthy subjects at different sizes of constant grip force. The results showed that the method was superior in representing changes in the causal link among multichannel signals characterized by different frequency bands and grip strength patterns. Complex information transfer between the cerebral cortex and the corresponding muscle groups during constant grip force output from the human upper limb. Furthermore, the sEMG of the flexor digitorum superficialis (FDS) in the low frequency band is the hub in the effective information transmission between the cortex and the muscle, while the importance of each frequency component in this transmission network becomes more dispersed as the grip strength grows, and the increase in coupling strength and node status is mainly in the γ band (30~60Hz). This study provides new ideas for deconstructing the mechanisms of neural control of muscle movements.

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. To address these challenges, we propose an ultralightweight fully asymmetric convolutional network for skin lesion segmentation, called ULFAC-Net. we use a parallel asymmetric convolutional (PAC) module to extract features instead of the traditional square convolution, and innovatively propose a PAC module with dual attention (Att-PAC) to enhance the feature representation. Based on the PAC and Att-PAC modules, we further propose a lightweight textual information submodule. To balance the number of parameters and performance of the model, we also hand-design an asymmetric encoder-decoder architecture. In this paper, we validate the effectiveness and robustness of the proposed ULFAC-Net on four publicly available skin lesion segmentation datasets (ISIC2018, ISBI2017, ISIC2016 and PH2 datasets). The experimental results show that ULFAC-Net achieves competitive segmentation performance with only 0.842 million(0.842M) parameters and 3.71 gigabytes of floating point operations (GFLOPs) compared to other state-of-the-art methods.

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. Therefore, in this study, we applied the self-supervised learning methods to improve the efficiency of resources usage. We employed a self-supervised approach to train deep multi-task convolutional neural network (CNN) for EEG-based emotion classification. First, six signal transformations were performed on unlabeled EEG data to construct the pretext task. Second, a multi-task CNN was used to perform signal transformation recognition on the transformed signals together with the original signals. After the signal transformation recognition network was trained, the convolutional layer network was frozen and the fully connected layer was reconstructed as emotion recognition network. Finally, the EEG data with affective labels were used to train the emotion recognition network to clarify the emotion. In this paper, we conduct extensive experiments from the data scaling perspective using the SEED, DEAP affective dataset. Results showed that the self-supervised learning methods can learn the internal representation of data and save computation time compared to the fully-supervised learning methods. In conclusion, our study suggests that the self-supervised machine learning model can improve the performance of emotion classification compared to the conventional fully supervised model.

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. Therefore, inspired by generative adversarial network (GAN), this study aims to propose an improved domain adaption network based on Wasserstein distance, which utilizes existing labeled data from multiple subjects (source domain) to improve the performance of MI classification on a single subject (target domain). Specifically, our proposed framework consists of three components, including a feature extractor, a domain discriminator, and a classifier. The feature extractor employs an attention mechanism and a variance layer to improve the discrimination of features extracted from different MI classes. Next, the domain discriminator adopts the Wasserstein matrix to measure the distance between source domain and target domain, and aligns the data distributions of source and target domain via adversarial learning strategy. Finally, the classifier uses the knowledge acquired from the source domain to predict the labels in the target domain. The proposed EEG-based MI classification framework was evaluated by two open-source datasets, the BCI Competition IV Datasets 2a and 2b. Our results demonstrated that the proposed framework could enhance the performance of EEG-based MI detection, achieving better classification results compared with several state-of-the-art algorithms. In conclusion, this study is promising in helping the neural rehabilitation of different neuropsychiatric diseases.

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. To achieve effective transfer, a double-stage transfer learning (DSTL) algorithm which applied transfer learning to both preprocessing stage and feature extraction stage of typical BCIs was proposed. First, Euclidean alignment (EA) was used to align EEG trials from different subjects. Second, aligned EEG trials in the source domain were reweighted by the distance between the covariance matrix of each trial in the source domain and the mean covariance matrix of the target domain. Lastly, after extracting spatial features with common spatial patterns (CSP), transfer component analysis (TCA) was adopted to reduce the differences between different domains further. Experiments on two public datasets in two transfer paradigms (multi-source to single-target (MTS) and single-source to single-target (STS)) verified the effectiveness of the proposed method. The proposed DSTL achieved better classification accuracy on two datasets: 84.64% and 77.16% in MTS, 73.38% and 68.58% in STS, which shows that DSTL performs better than other state-of-the-art methods. The proposed DSTL can reduce the difference between the source domain and the target domain, providing a new method for EEG data classification without training dataset.

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. To solve the above problems, Tensor-Train(TT) decomposition is adopted to analyze the EEG tensor. Meanwhile, sparse regularization term can then be added to TT decomposition, resulting in a sparse regular TT decomposition (SR-TT). The SR-TT algorithm is proposed in this paper, which has higher accuracy and stronger generalization ability than state-of-the-art decomposition methods. The SR-TT algorithm was verified with BCI competition III and BCI competition IV dataset and achieved 86.38% and 85.36% classification accuracies, respectively. Meanwhile, compared with traditional tensor decomposition (Tucker and CP) method, the computational efficiency of the proposed algorithm was improved by 16.49 and 31.08 times in BCI competition III and 20.72 and 29.45 times more efficient in BCI competition IV. Besides, the method can leverage tensor decomposition to extract spatial features, and the analysis is performed by pairs of brain topography visualizations to show the changes of active brain regions under the task condition. In conclusion, the proposed SR-TT algorithm in the paper provides a novel insight for tensor EEG analysis.

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. However, when EEG data is taken as input in raw time series, traditional 1D-CNNs are unable to acquire both frequency domain and channel association information. To solve this problem, this study proposes a novel algorithm by inserting two modules into CNN. One is the Filter Band Combination (FBC) Module, which preserves as many frequency domain features as possible while maintaining the time domain characteristics of EEG. Another module is Multi-View structure that can extract features from the output of FBC module. To prevent over fitting, we used a cosine annealing algorithm with restart strategy to update the learning rate. The proposed algorithm was validated on the BCI competition dataset and the experiment dataset, using accuracy, standard deviation, and kappa coefficient. Compared with traditional decoding algorithms, our proposed algorithm achieved an improvement of the maximum average correct rate of 6.6% on the motion imagery 4-classes recognition mission and 11.3% on the 2-classes classification task.

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. For these problems, combined with the self-attention characteristics of Transformer, an end-to-end person re-identification model (twinsReID) is designed that integrates feature information between levels in this article. For Transformer, the output of each layer is the correlation between its previous layer and other elements. This operation is equivalent to the global receptive field because each element needs to calculate the correlation with other elements, and the calculation is simple, so its cost is small. From these perspectives, Transformer has certain advantages over CNN's convolution operation. This paper uses Twins-SVT Transformer to replace the CNN network, combines the features extracted from the two different stages and divides them into two branches. First, convolve the feature map to obtain a fine-grained feature map, perform global adaptive average pooling on the second branch to obtain the feature vector. Then divide the feature map level into two sections, perform global adaptive average pooling on each. These three feature vectors are obtained and sent to the Triplet Loss respectively. After sending the feature vectors to the fully connected layer, the output is input to the Cross-Entropy Loss and Center-Loss. The model is verified On the Market-1501 dataset in the experiments. The mAP/rank1 index reaches 85.4%/93.7%, and reaches 93.6%/94.9% after reranking. The statistics of the parameters show that the parameters of the model are less than those of the traditional CNN model.

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. First, single-modality fMRI can only detect the cerebral hemodynamics but not the neuronal activity, lacking detailed information of the neurovascular coupling process in the brain. Second, the RE measurement utilized to quantify the broken detailed balance may be biased and have no finite upper bound, limiting its application in high dimensional signal domains. In this study, a neurovascular coupling strategy based on concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recordings was proposed to take both the cerebral hemedynamics and neuronal activity into consideration in assessing broken detailed balance of the brain. Furthermore, the generalized relative entropy (GRE) was employed to reduce the bias associated with the conventional RE measure. Our results demonstrated that the proposed framework showed higher classification accuracy (82.48%) to identify different levels of working memory loads than conventional methods. In addition, our results revealed that the broken detailed balance was significantly stronger when subjects performed cognitively demanding tasks (P<0.05) and was highly correlated with the neurovascular coupling models derived from the EEG θ and α bands, respectively. In conclusion, our findings provide an advanced framework to accurately classify various levels of working memory with the broken detailed balance of human brain and can be extended to explore the underlying broken detailed balance related to other cognitive behaviors and diseases.

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. To effectively transfer data from a source to target domain, a multi-manifold embedding domain adaptive algorithm is proposed for BCI. First, we aligned the EEG covariance matrix in the Riemannian manifold and extracted the characteristics of each source domain in the tangent space to reflect the differences between different source domains. Subsequently, we mapped the extracted characteristics to the Grassmann manifold to obtain a common feature representation. In domain adaptation, the geometric and statistical attributes of EEG data were considered simultaneously, and the target domain divergence matrix was updated with pseudo-labels to maximize the inter-class distance and minimize the intra-class distance. Datasets generated via BCIs were used to verify the effectiveness of the algorithm. Under two experimental paradigms, namely single-source to single-target and multi-source to single-target, the average accuracy of the algorithm on three datasets was 73.31% and 81.02%, respectively, which is more than that of several state-of-the-art EEG cross-domain classification approaches. Our multi-manifold embedded domain adaptive method achieved satisfactory results on EEG transfer learning. The method can achieve effective EEG classification without a same subject's training set.

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. The emotional EEG signals of 32 healthy subjects were from an open-source dataset (DEAP). The wavelet packets were first applied to extract the time-frequency features of the EEG signals, and then the features were used to construct the enhanced SPD matrix. A supervised dimensionality reduction algorithm was then designed on the Riemannian manifold to reduce the high dimensionality of the SPD matrices, gather samples of the same labels together, and separate samples of different labels as much as possible. Finally, the samples were mapped to the tangent space, and the K-nearest neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM) method were employed for classification. The proposed method achieved an average accuracy of 91.86%, 91.84% on the valence and arousal recognition tasks. Furthermore, we also obtained the superior accuracy of 86.71% on the four-class recognition task, demonstrated the superiority over state-of-the-art emotion recognition methods.

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. The goal of this study is to quantify the alterations of inter-muscular coherence (IMC) among upper extremity muscles that have been shown to be synergistically or non-synergistically activated in stroke survivors. In a three-dimensional isometric force matching task, surface EMG signals are collected from 6 age-matched, neurologically intact healthy subjects and 10 stroke patients, while the target force space is divided into 8 subspaces. According to the results of muscle synergy identification with non-negative matrix factorization algorithm, muscle pairs are classified as synergistic and non-synergistic. In both control and stroke groups, IMC is then calculated for all available muscle pairs. The results show that synergistic muscle pairs have higher coherence in both groups. Furthermore, anterior and middle deltoids, identified as synergistic muscles in both groups, exhibited significantly weaker IMC at alpha band in stroke patients. The anterior and posterior deltoids, identified as synergistic muscles only in stroke patients, revealed significantly higher IMC in stroke group at low gamma band. On the contrary, anterior deltoid and pectoralis major, identified as synergistic muscles in control group only, revealed significantly higher IMC in control group in alpha band. The results of muscle synergy and IMC analyses provide congruent and complementary information for investigating the mechanism that underlies post-stroke motor recovery.

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. And an epileptic signal classification method based on SLPCCA is proposed. Firstly, the power spectral density and the fluctuation index of the frequency slice wavelet transform are extracted as features from the EEG fragments. Next, SLPCCA obtains the optimal projection direction by maximizing the weight correlation between the paired samples in the class and their neighbors. And the projection combination of original features in the optimal direction is the fusion feature. The fusion features are then input into LS-SVM for training and testing. This method is verified on the Bonn dataset and the CHB-MIT dataset and gets good results. On various classification tasks of Bonn data set, the proposed method achieves an average classification accuracy of 99.16%. On the binary classification task of the inter-seizure and seizure epileptic EEG of the CHB-MIT dataset, the proposed method achieves an average accuracy of 97.18%. The experimental results show that the algorithm achieves excellent results compared with several state-of-the-art methods. In addition, the parameter sensitivity of SLPCCA and the relationship between the dimension of the fusion features and the classification results are discussed. Therefore, the stability and effectiveness of the method are further verified.

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. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain. RESULTS: The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal. Comparison with Existing Method(s): Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis. CONCLUSIONS: The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.

[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. Specifically, the triceps brachii (TB) had relatively high coupling strength with the middle deltoid (MD) and posterior deltoid (PD), and the intermuscular function was closely connected. However, the biceps brachii (BB) was independent of other muscles. The intermuscular coupling network had scale differences. MVMD-Copula MI can quantitatively describe the relationship of multi-scale intermuscular coupling strength, which has good application prospects.

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. AD and Depression commonly occur together with overlapped symptoms (depressed mood, anxiety, apathy, and cognitive deficits.) and pose diagnostic challenges early in the clinical presentation. Understanding their relationship is critical for advancing treatment strategies, but the interaction remains poorly studied and thus often leads to a rapid decline in functioning. Modern systems and control theory offer a wealth of novel methods and concepts to assess the important property of a complex control system, such as the brain. In particular, the brain controllability analysis captures the ability to guide the brain behavior from an initial state (healthy or diseased) to a desired state in finite time, with suitable choice of inputs such as external or internal stimuli. The controllability property of the brain's dynamic processes will advance our understanding of the emergence and progression of brain diseases and thus helpful in the early diagnosis and novel treatment approaches. This study aims to assess the brain controllability differences between mild cognitive impairment (MCI), as prodromal AD, and Depression. This study used diffusion tensor imaging (DTI) data from 60 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI): 15 cognitively normal subjects and 45 patients with MCI, including 15 early MCI (EMCI) patients without depression, 15 EMCI patients with mild depression (EMCID), and 15 late MCI (LMCI) patients without depression. The structural brain network was firstly constructed and the brain controllability was characterized for each participant. The controllability of default mode network (DMN) and its sub-regions were then compared across groups in a structural basis. Results indicated that the brain average controllability of DMN in EMCI, LMCI, and EMCID were significantly decreased compared to healthy subjects (P < 0.05). The EMCI and LMCI groups also showed significantly greater average controllability of DMN versus the EMCID group. Furthermore, compared to healthy subjects, the regional controllability of the left/right superior prefrontal cortex and the left/right cingulate gyrus in the EMCID group showed a significant decrease (P < 0.01). Among these regions, the left superior prefrontal region's controllability was significantly decreased (P < 0.05) in the EMCID group compared with EMCI and LMCI groups. Our results provide a new perspective in understanding depressive symptoms in MCI patients and provide potential biomarkers for diagnosing depression from MCI and AD.

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). Then Lasso regression is used to select significant features in each atom synchronously in the horizontal direction, and the KNN-based method is used to clean up noise atoms in the vertical direction. Finally, an SRC method by training samples linearly representing test samples was implemented in classification. RESULTS: The results show the necessity and rationality of TDDO-SRC method. The highest average classification accuracy of 86.5% and 92.4% is obtained on two public datasets. COMPARISON WITH EXISTING METHOD(S): The proposed method has more superior classification accuracy compared to traditional methods and existing winners' methods. CONCLUSIONS: The quality of dictionary construction has a great impact on the robustness of SRC. And compared with the original SRC, the classification accuracy of the optimized TDDO-SRC is greatly improved.