Abstract:Alzheimer’s disease （AD） is an irreversible neurodegenerative disease， whose early medical intervention is of great significance to control and improve the condition. In recent years， deep learning methods have been widely used by researchers to analyze magnetic resonance imaging （MRI） of AD for early diagnosis. However， the changes of brain structure are less different from those of normal people in the early stage， and the existing single-scale analysis methods are difficult to capture these subtle differences. Aiming at the above problem， this paper proposes a feature enhanced pyramid network （FEPN） for early diagnosis of AD. The high-level features are supplemented by the contextual information extracted from the designed shallow feature re-extraction， and the fusion weights are calculated to guide the fusion of high-level and low-level feature maps， which enhance the interaction of contextual information and the matching degree of multi-scale feature fusion. The Alzheimer datasets published by Kaggle are employed to conduct comparison experiments to verify the performance of the proposed approach. The comparison experiment employs the Alzheimer dataset published by Kaggle to verify the performance. Compared with related methods， FEPN achieves the SOTA classification accuracy of MRI of four AD brain states （non-demented， very mild demented， mild demented， moderate demented）.

Abstract:Motor control is a process of multifaceted coordination and information interaction among neural， motor and sensory functions. The relationships between motion and physiological information in the motor control system is helpful to understand the mechanism of human motion control. Therefore， to explore the causal relationship and the evolutionary law between electroencephalogram （EEG） and acceleration （ACC） signals during upper limb movement and rest， we apply the coherence method in this study. Firstly， the EEG and ACC signals of 7 subjects are preprocessed to remove the interference components in the signals. Secondly， the coherence values between EEG and ACC signals during the resting， motion-action and motion-maintaining states are calculated respectively， and the significant area is then calculated by the threshold index of significant coherence. The results show that the significant areas in the motion-action state are larger than that of in the motion-maintaining state， and in the motion maintenance state is larger than that of in the resting state. Furthermore， the significant areas between EEG signals of C3 and C4 channels and ACC signals are more significant in the contralateral motor cortex during left and right upper limb movements. These results indicate that there are significant differences between EEG and ACC signals during the resting， motion-action and motion-maintaining states of upper limb movements， which can be helpful to deeply understand the neuromotor control mechanism， and also provide a new quantitative index and the theoretical basis for the assessment of motor function and the early diagnosis of motor dysfunction diseases.

Abstract:Diffusion magnetic resonance imaging （dMRI） is an important medical imaging tool for the non-invasive detection of microstructures in biological tissues. Among others， intravoxel incoherent motion （IVIM） is a widely used dMRI model to separate diffusion and microvascular perfusion. Conventional methods to resolve IVIM parameters rely on fitting a biexponential model from multi-b-value dMRI data （typically ≥10 b-values）， which requires a relatively long acquisition time. Such an acquisition is challenging for IVIM imaging of the body， such as placental IVIM， which is strongly influenced by both fetal and maternal motions. Deep learning models can accelerate the dMRI acquisition using a subset of the q-space data. However， common deep learning based on convolutional neural networks is not relevant to biophysical models and， therefore， the outputs of the network are difficult to interpret. Here， this work combines sparse coding with deep learning to develop a sparse coding based deep neural network for the IVIM parameter estimation that takes advantage of the feature representation of deep networks while incorporating a potential bi-exponential model to estimate the microcirculation parameters of the placenta. Compared with other algorithms， the proposed algorithm demonstrates advantages in accuracy and generalizability.

Abstract:An intracranial hemorrhage （ICH） is a kind of severe emergency that occurs suddenly in patients’ brain with strong symptoms and high mortality. So it is of great significance to diagnose ICH automatically and quickly based on brain CT images. However， effective clinical application requires not only the accuracy， speed and interpretation ability of models， but also especially the emphasis given to the missed detection of bleeding. Therefore， cost-sensitive Faster R-CNN is proposed in this paper to diagnose ICH， through an automatic adjustment mechanism for the proportion of training samples and a hyperparameter introduced to loss function to measure the importance of positive samples. It can pay more attention to the missed detection situations to improve the detection effect， and diagnose ICH by located target region. A network structure with optimal performance and appropriate parameter is selected for good effect of detection and diagnosis through experiments. And then， results are measured by several indexes. It is shown that the cost-sensitive Faster R-CNN model can detect bleeding well by focusing on missed checks， so as to improve the diagnosis effect under the unbalanced cost.

Abstract:Intracranial aneurysm is a common cerebral vascular disease with a relatively high lethiferous and disable rate. An image-based intelligent and accurate diagnosis method of the disease is urgently needed by the clinic in recent years， for which the accurate segmentation of the vessels and aneurysms is very essential. In this work， we present a novel segmentation framework for the multi-structure intracranial vessels with aneurysms. An adaptive image sampling method is designed using the prior gray-level vascular features， and a Dense mechanism-based network is proposed for the vessel segmentation. Time-of-flight magnetic resonance angiography images of 135 patients （age： 54.7±12.7， 75 males） with intracranial aneurysms are collected for training and testing the framework. Compared with the sampling in the original space and image compression （mean DSC： 0.829 and 0.780）， the adaptive sampling can obviously improve the accuracy of the vessel segmentation （mean DSC： 0.858）. The Dense mechanism-based network can achieve better segmentation result while using less calculation space than the traditional models of 3D UNet， SegNet and DeepLabV3+ （mean DSC： 0.854，0.824 and 0.800）. It also shows good robustness for the segmentation of aneurysms with various locations and sizes.

Abstract:With the continuous development of medical imaging data， longitudinal data analysis is gradually becoming an important research direction to understand and trace the process of the Alzheimer’s disease （AD）. At present， many longitudinal data analysis methods have been proposed， among which multi-task learning is widely used， which can integrate imaging data of multiple time points to improve the generalization ability of the model. Most existing methods can identify shared features at different time points， but these features will contain a certain amount of noise. At the same time， potential associations of disease progression at different time points remain under explored. In this paper， we propose a parameter decomposition and relation-induced multi-task learning （PDRIMTL） method to identify features from longitudinal data. The method can not only identify shared features after noise removal and improve the robustness of shared features， but also can model the intrinsic associations between different time points. The results show that the model can effectively improve the accuracy of AD identification on structural magnetic resonance imaging （sMRI） data at different time points.

Abstract:Effectively analyzing， processing and accurately classifying epileptic electroencephalographic （EEG） signals can further improve the problem of epilepsy detection. Therefore， various deep learning approaches have been gradually applied to this problem， such as using the BiLSTM model to process the 1D time series data of epileptic EEG. To further improve the accuracy of epileptic EEG classification， the 1D time series data of epileptic EEG is converted into 2D images and the EfficientNetV2 model is used to achieve binary classification for epilepsy detection in this paper. At the same time， the gradient-weighted class activation mapping （Grad-CAM） is introduced for visual analysis of 2D images classification. By performing classification experiments on a pre-processed version of the epilepsy EEG signal dataset from the University of Bern， Germany， the EfficientNetV2 model achieves the accuracy of 98.69%， which is better than the BiLSTM model. The result indicates that the EfficientNetV2 model can effectively achieve epileptic EEG classification by 2D EEG images with higher classification accuracy.

Abstract:Functional connectivity （FC） can represent the ability of brain regions to work together. At present， a combination of dynamic functional connectivity （DFC） and cluster analysis is widely used to study the significant difference analysis and classification of diseases. However， in the existing study， there is no clear standard for the determination of the number of clusters and the selection of clustering results， and the traditional DFC cannot represent the FC information of different frequencies. Therefore， this paper studies the clustering related factors of intrinsic frequency DFC in MRI signal of mild cognitive impairment （MCI）. First， the noise-assisted multivariate empirical mode decomposition of the time course （TC） data is performed and the DFC is calculated. Then， the cluster is analyzed through the evaluation-assisted clustering method， and the least square method is used to fit the clustering results. Finally， classifier is used for classification. The contribution of this paper is to suggest a more reasonable clustering method and a more number of clusters to obtain functional connections at different intrinsic frequencies. In the experiment， we used the Alzheimer’s disease neuroimaging （ANDI） database to test the proposed method. The experimental results show that the accuracy of supervised clustering used in this paper is higher than that of unsupervised clustering； the classification accuracy of DFC with natural frequency is higher than that of traditional DFC； the least square fitting can improve classification accuracy.

Abstract:A new electroencephalogram （EEG） emotion recognition method based on deep convolutional neural network-joint adaptation network （CNN-JAN） is presented. It incorporates the idea of joint adaptation in transfer learning into deep convolutional networks. Firstly， the model uses a rectangular convolution kernel to extract the deep emotion-related spatial features between EEG data channels. Then， the extracted spatial features are input into the adaptation layer with multi-kernel joint maximum mean discrepancy （MK-JMMD） for transfer learning， aiming to reduce the distribution differences between the source and target domains. The experiments are carried out on differential entropy features and differential causality features of EEG data from the SEED dataset to verify the effectiveness and advantages of the proposed method. As a result， the within-subject emotion classification accuracy on differential entropy features reaches 84.01%， and the cross-subject emotion classification accuracy is also improved compared with other current popular transfer learning methods.

Abstract:Students’ learning situation is closely related to their classroom attention state. In order to explore whether attention training can improve classroom attention， the electroencephalogram （EEG） signals of non-attention and attention states of ten students before and after α music training are collected and compared. It is worth noting that EEG signal is dynamic in nature and has the characteristics of low signal-to-noise ratio and high redundancy. In order to avoid the problem of poor recognition of EEG signals directly through neural network， 11 features of signal sample entropy （SampEn）， energy and energy ratio of each band are extracted， and these features are fused into multi-feature images as the input of neural network model. In addition， the weighted fusion of AlexNet and VGG11 network models is used to form a double convolution neural network （CNN）， which can further improve the performance of image classification. The results show that the performance of the fusion model with double CNN can achieve a better performance compared with the model with single CNN. In particular， the recognition accuracy of the proposed model can reach 97.53%. It can be found that after α music training， the EEG features of the subjects are significantly different from those before， and the classification accuracy of the network model can be 4% higher than that before training. This observations show that the considered α music training can improve the attention level of healthy students.

Abstract:Hemorrhage volume is an important indicator for the grading of intracerebral hemorrhage disease， the determination of treatment options， and the judgment of prognosis. However， because of the complexity of the brain structure and the variety of morphology and location of the hematoma， accurate and reliable segmentation of the hematoma and measurement of the amount of hemorrhage are extremely difficult. This paper presents an algorithm for cerebral hematoma segmentation and blood volume measurement based on a self-attention mechanism deep learning network. First， to overcome the complexity of brain structure and make up for the shortcomings that convolution module can only perform linear operations and extract local features， a self-attention module is introduced at the end of the encoder of the segmentation network， and through higher order operations， the feature association properties of the whole domain of the image are extracted and the hematoma is extracted from a global perspective. Second， a channel and spatial attention module is introduced to obtain weights on the individual channels and feature regions through training learning， by which useful information is highlighted and useless information is suppressed. Finally， the hemorrhage volume is calculated based on the hematoma segmentation results of multislice CT imaging slices in patients with intracerebral hemorrhage. The experimental results on the real CT imaging data set of intracerebral hemorrhage show that the proposed algorithm achieves better results on cerebral hematoma segmentation and hemorrhage volume measurement in multiple cases， and even is still relatively effective in the case of irregular shape or close to skull.

Abstract:Most of psychophysiological computing （PPC） studies are under the experimental environments of synchronization theory hypothesis， however neurophysiological representations have asynchronous properties， which cannot be precisely and effectively described in real time using synchronized recording technology. It is being the first issue of PPC to resolve how to recode these asynchronous multi-modality neurophysiological activities with low-power， low-redundancy， real-time and accurate. For this issue， this study focuses on the goals of microscopic neurophysiological activities and macroscopic psychological variables， resolves the design challenges of asynchronous multimodality physiological information recording scheme and corresponding passive physiological signals sensing technology， and designs and develops the first asynchronous physiological process unit （PPU）. The PPU has the characteristics of low power consumption， high time series precision， high computing performance and strong anti-interference ability. Finally，we look forward to the future of PPU applied in the research area of brain science and brain-like computing.

Abstract:To address the problem that artificially extracted redundant feature sets and irrelevant feature sets lead to the degradation of human activity recognition classification performance of wearable sensor， this paper proposes a human activity recognition method based on heuristic integrated feature selection. The method first selects the feature set containing power spectral density （PSD） for recognizing confusing activities. Then， on this basis， the method screens out the lowly correlated feature subsets with the help of Pearson correlation coefficient （PCC） method， then uses an improved sine cosine algorithm （SCA） for features and obtains the optimal feature subset by screening the feature twice. The experimental results show that the feature subset dimension after using this method in the data set collected in the laboratory is 34， and the recognition accuracy rate reaches 98.21%. In the public SCUT-NAA data set for comparison experiments， the feature subset dimension is 39， lower than the feature dimension of previous research methods， and the recognition accuracy rate reaches 96.51%.

Abstract:Based on the definition of gait parameters， this paper proposes and studies a method of collecting and extracting gait parameters using the Microsoft’s Azure Kinect maker-free motion capture system（hereinafter referred to as Kinect system）. At the same time， adaptive filtering， exponential filtering， Kalman filtering and no filtering conditions are used in data processing to improve the smoothness of gait data. In order to evaluate the accuracy of Kinect system and the effectiveness of filtering， the results of extracted gait parameter are statistically compared with those of the Qualisys marker-based motion capture system （Company of Sweden， hereinafter referred to as Q marker-based） in the synchronous experiment， and the different filtering methods are evaluated accordingly. The results show that， in general， the Kinect system has a high consistency with the Q marker-based， and the results under the three filtering conditions all fall within the 95% consistency limit. In terms of their gait parameters， the results of the gait speed are quite different under all filtering conditions， which cannot be applied. For other parameters， adaptive filtering and Kalman filtering show good consistency. Kinect system can accurately calculate the gait parameters of healthy people by applying the proposed method and smoothing it with Kalman filtering， and it can replace the marker-based device in some cases.

Abstract:The diagnosis of H-type hypertension requires the determination of the patient’s plasma homocysteine content， which is inefficient and has a wound. Chinese pulse diagnosis helps doctors diagnose H-type hypertension by analyzing patient’s pulse activity and combining inquiry information. Therefore， we put forward a pulse-taking diagnosis classifiction model based on hybrid deep learning model， which can extract the local features via convolutional neural network（CNN） block， and long-term dependency features via Bi-directional long short-term memory（BiLSTM） block. The data come from 325 suspected cases of pulse diagnosis collected by Longhua Hospital affiliated to Shanghai University of Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We compare the proposed model with other machine learning models on the pulse diagnosis data respectively. The sensitivity， specificity， accuracy， F₁-score， receiver operating characteristic（ROC）area under curve （AUC） values of the proposed model are 79.71%， 69.56%， 77.17%， 83.96%， 0.850 0， respectively， higher than the performance of other machine learning models. The results show that our model has good performance and has good reference value for the clinical diagnosis of traditional Chinese medicine.

Abstract:Nowadays， developments of technology have allowed the generation of huge amounts of streaming data， such as network traffic flows， web click stream， video stream， event stream and semantic concept stream. Therefore， data stream mining has become a hot research topic and its goal is to extract hidden knowledge/patterns from continuous stream data. Clustering， as one of the most important problems in stream mining， has been highly explored recently. However， data stream clustering algorithms differ from traditional static data clustering algorithms in many aspects， and have more constraints such as bounded memory， single-pass， real-time response and concept-drift detection. In this paper， we survey the state-of-the-art data stream clustering algorithms. Firstly， mining constraints are identified. Then a general model for stream clustering is given， and its association with traditional data clustering is described. Finally， some further research issues in this domain are put forward.

Abstract:Speech synthesis technology is becoming more mature. In order to improve the quality of synthetic emotional speech， this study proposes a method combining end-to-end emotional speech synthesis with prosodic correction. Based on the Tacotron model， the prosodic parameters are modified to improve the emotion expression power of the synthetic system. Tacotron model is first trained with a large neutral corpus， and then a small emotional corpus is used to train and synthesize emotional speech. Then the Praat acoustic analysis tool is used to analyze the prosodic features of emotional speech in the corpus and summarize the parameters of different emotional states. Finally， with the help of this rule， the fundamental frequency， duration and energy of the corresponding emotional speech synthesized by Tacotron are modified to make the emotional expression more accurate. The results of objective emotion recognition experiment and subjective evaluation show that this method can synthesize more natural and expressive emotional speech.

Abstract:An IP core of low hardware-cost 256-point fast Fourier transform （FFT） processor is designed based on field programmable gate array （FPGA）. In order to reduce the complexity of twiddle factor calculation， the radix-2⁴ algorithm based on decimation in frequency and the single-path delay feedback （SDF） pipelined architecture are adopted. For reducing hardware-cost， a cascade canonical signed digit （CSD） complex multiplier instead of conventional Booth multiplier is proposed for the operation of twiddle factor W256i multiplied by the corresponding sequences. Also， the proposed cascade CSD multiplier can remove read only memory （ROM） for storing coefficients of twiddle factors. The IP core is synthesized by using QUARTUS PRIME tool and is implemented on Cyclone 10LP FPGA. The result shows that the proposed FFT design can work under a maximum clock frequency of 100 MHz which occupies only 3 978 logic elements （LEs） and 6 456 memory bits （MBs） hardware-resource for 24-bit signed number FFT operation.

Abstract:The storage solutions in the market cannot meet the requirements for storage speed and device volume with specific functional requirements. So， we design a SD3.0 version TF card controller based on field programmable gate array（FPGA）control， aiming to achieve higher-speed data storage while occupying the smallest volume. Through a self-designed small data acquisition card， the 24-bit-wide data are finally stored into the TF card through DDR3， FIFO， RAM， and two-level buffer. This paper introduces the scheme design from two aspects of hardware and software. The former mainly includes the circuit technology， acquisition card index and board-level signal integrity verification； the latter mainly includes the storage process， RTL-level verification and the TF card test. Experimental results show that the proposed PCB circuit can provide the voltage conversion and data storage functions required by SD3.0 protocol， and the board has stable functions and high integration. The speed of TF cards exceeds 60 MB/s with a long time. It has stable performance and good versatility. The experiment meets the design requirements， and it can also provide a solution for miniaturized storage experiments.