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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Li, Yongchao Chen, Nan Wang, Yin Yang, Lin Zheng, Weihao Yao, Zhijun Hu, Bin Gansu Province Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education Lanzhou China

ISBN: (纸本)9781728162676

Conventional hyper-network coefficients ignore the weighted hyper-edge information which could be vital in researching the specificity of brain disease. Functional hyper-networks for 64 healthy controls (HC) and 56 patients with major depressive disorder (MDD) were constructed using the least absolute shrinkage and selection operator (Lasso). Not only the classical topology metrics but also a novel hyper-edge weight (HEW) attribute were extracted as features to promote the functional-based auto-diagnosis accuracy of MDD. We compared the categorization performance of each hyper-network coefficient. A multi-feature ensemble model was applied to fuse different kinds of features. We obtained 82.15 % accuracy with the classical hyper-network clustering coefficient (HCC) and 84.08 % accuracy with the HEW attribute on the MDD dataset. The performance was further improved to 89.24% by combining all the properties of the hyper-networks. The multi-feature ensemble model combining different hyper-network coefficients provides new insights into the automatic diagnosis with diverse information of MDD. © 2021 IEEE.

关键词： Topology

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Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook

arXiv

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arXiv 2023年

作者： Zhang, Huang Wang, Changshuo Tian, Shengwei Lu, Baoli Zhang, Liping Ning, Xin Bai, Xiao The School of Software Xinjiang University Xinjiang830000 China The Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China Cognitive Computing Technology Joint Laboratory Wave Group Beijing102208 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China The School of Computer Science and Engineering Beihang University Beijing100083 China

In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in processing regular structured 2D grid image data, there are still great challenges in processing irregular, unstructured point cloud data. Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task. Therefore, the purpose of this paper is to provide researchers in this field with the latest research progress and future trends. First, we introduce point cloud acquisition, characteristics, and challenges. Second, we review 3D data representations, storage formats, and commonly used datasets for point cloud classification. We then summarize deep learning-based methods for point cloud classification and complement recent research work. Next, we compare and analyze the performance of the main methods. Finally, we discuss some challenges and future directions for point cloud classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

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Correction to: Retinal vessel extraction using dynamic multi-scale matched filtering and dynamic threshold processing based on histogram fitting

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Machine Vision and Applications 2019年第3期30卷 541-541页

作者： Duoduo Gou Ying Wei Hong Fu Ning Yan Shandong University WAVE Joint Laboratory for Cognitive Computing Technology Chu Hai College of Higher Education

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Depression Detection using Resting State Three-channel EEG Signal

arXiv

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arXiv 2020年

作者： Shi, Qiuxia Liu, Ang Chen, Rongyan Shen, Jian Zhao, Qinglin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Ministry of Education China

In universal environment, a patient-friendly inexpensive method is needed to realize the early diagnosis of depression, which is believed to be an effective way to reduce the mortality of depression. The purpose of this study is only to collect EEG signal from three electrodes Fp1, Fpz and Fp2, then the linear and nonlinear features of EEG used to classify depression patients and healthy controls. The EEG recordings were carried out on a group of 18 medication-free depressive patients and 25 gender and age matched controls. In this paper, the selected features include three linear (maximum, mean and center values of the power) and three nonlinear features (correlation dimension, Renyi entropy and C0 complexity). The accuracy and effectiveness of classification model between depressive and control subjects were calculated using leave-one-out cross-validation. The experimental results indicate that selected three channel EEG and features can distinguish the subjects between depression and normal beings, the classification accuracy is 72.25%. It is hoped that the performed results can provide more choices for the early diagnosis of depression in a universal environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Adversarial U-Network for Predicting Blood Oxygen Level-Dependent Time Series

Adversarial U-Network for Predicting Blood Oxygen Level-Depe...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Cong Bao Weihao Zheng Qin Zhang Songyu Yang Zhijun Yao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Lanzhou China

Functional magnetic resonance imaging (fMRI) plays a vital role in brain science as it measures and maps brain activity through the analysis of blood flow changes, offering valuable insights into cognitive functions and neural processes. However, due to the intricacy and dynamism of brain activity, conventional approaches failed to accurately predict the blood oxygen level-dependent (BOLD) time series in fMRI data. To tackle this issue, we proposed an end-to-end adversarial U-network (AUN) to verify the predictability of existing BOLD signals in primary cortex (i.e., primary visual, primary motor and primary sensory) and higher cortex(i.e., dorsolateral prefrontal and posterior cingulate). The model combined the U-network architecture and adversarial strategy to ensure that the predicted results capture both the intricate nonlinear details and the overall distribution characteristics. We performed the experiment using the Human Connectome Project (HCP) database. The results demonstrated the predictability of both primary and higher cortex, with primary cortex showing higher predictability. Additionally, the AUN performed better than other popular methods. We also found the improvement in dynamic functional connectivity (dFC) metrics through accurate prediction. The above results confirm the feasibility of predicting BOLD signals and their potential application in clinical settings. 1

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Prefrontal Internal Event-Driven Analysis of Dynamical Electroencephalographic Biomarkers in Depression During Emotional Auditory Task

SSRN

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SSRN 2024年

作者： Zhao, Qinglin Cui, Kunbo Jiang, Hua Wu, Zhongqing Zhang, Lixin Zhao, Mingqi Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Medical Technology Beijing Institute of Technology Beijing100081 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai200000 China Joint Research Center for Cognitive Neurosensor Technology Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou730000 China

The prefrontal cortex is considered as one of the key brain regions for the study of affective dysfunction in depression. The study of neural processes in this region during specific emotional tasks may enhance our understanding on the mechanisms of depression. One powerful, direct and convenient tool is electroencephalography (EEG);yet in many experimental paradigms, the absence of external events limits the effectiveness of resolving such emotional task-specific prefrontal and brain-wide electroencephalographic dynamics. To address this issue, we analyzed 64-channel EEG data acquired from 55 healthy and 55 depressed participants during a three-polar (positive, neutral and negative) emotional auditory task in a prefrontal internal event-driven approach. Specifically, we extracted depression-related prefrontal internal events according to signals acquired from Fp1, Fpz, and Fp2 by statistically comparing the microstates identified with an unsupervised Gaussian mixture model. We then examined such event-related neural dynamics in terms of power change in neural oscillations and cross-oscillation interaction in a reconstructed brain source space. Our results suggested that the prefrontal events can be used to resolve abnormal neural dynamics in depression. Our results revealed emotional process-specific and internal event-related abnormalities dynamically in the brain source space, as evaluated by internal event-related changes in power of neural oscillations and in modulations of low frequency phases to high frequency amplitudes. Overall, our findings present new insights for dynamical electroencephalographic biomarkers in depression, which potentially provide EEG signal decoding solutions for EEG feedback-based closed loop intervention of depression. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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A novel decision tree for depression recognition in speech

arXiv

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arXiv 2020年

作者： Liu, Zhenyu Wang, Dongyu Zhang, Lan Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Beijing China Lanzhou University Second Hospital Lanzhou China

Depression is a common mental disorder worldwide which causes a range of serious outcomes. The diagnosis of depression relies on patient-reported scales and psychiatrist’s interview which may lead to subjective bias. In recent years, more and more researchers are devoted to depression recognition in speech, which may be an effective and objective indicator. This study proposes a new speech segment fusion method based on decision tree to improve the depression recognition accuracy and conducts a validation on a sample of 52 subjects (23 depressed patients and 29 healthy controls). The recognition accuracy are 75.8% and 68.5% for male and female respectively on gender-dependent models. It can be concluded from the data that the proposed decision tree model can improve the depression classification performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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Design and Verification of an Aromatherapy Feedback System for Mental Fatigue Based on Physiological Signals

Design and Verification of an Aromatherapy Feedback System f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tao Sun Fuze Tian Hua Jiang Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Institutes for Biological Sciences Shanghai Institutes for Biological Sciences Chines Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

Mental fatigue is a prevalent issue in contemporary society and can negatively affect physical performance and concentration, increasing the likelihood of adverse consequences due to inattention during productive activities. Therefore, it becomes increasingly important to address and eliminate fatigue within a specific period of time. Aromatherapy, as a form of Complementary Alternative Medicine (CAM), is a non-invasive, cost-effective, and efficient method to combat fatigue. Previous studies have assessed the effects of specific aromatherapy oils using scales, but there is a lack of objective and reliable physiological indicators to prove the effectiveness of aromatherapy. Hence, this paper seeks to establish a model illustrating the effects of aromatic essential oil gases on the human body. A multimodal physiological fatigue signal acquisition system that integrates aromatherapy feedback was designed. In addition, an experimental paradigm was developed to explore the potential of aromatherapy in mitigating mental fatigue. Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were collected, allowing for the analysis of time-frequency domain features in EEG and ECG signals, as well as Heart Rate Variability (HRV) features in ECG signals. Our findings indicate that specific aromatic gases demonstrate effectiveness in reducing mental fatigue. Furthermore, we employed the Support Vector Machine (SVM) algorithm to classify the state of human mental fatigue. Based on the classification results, the release of aromatic gas was controlled to provide targeted aromatic feedback. This innovative approach offers a promising avenue for objectively assessing and addressing mental fatigue through aromatherapy interventions.

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Erratum to:Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2024年第10期25卷 801-802页

作者： Weihao ZHENG Qin ZHANG Ziyang ZHAO Pengfei ZHANG Leilei ZHAO Xiaomin WANG Songyu YANG Jing ZHANG Zhijun YAO Bin HU Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and EngineeringLanzhou UniversityLanzhou730000China Second Clinical School Lanzhou UniversityLanzhou730030China Department of Magnetic Resonance Lanzhou University Second HospitalLanzhou730030China Gansu Province Clinical Research Center for Functional and Molecular Imaging Lanzhou730030China School of Medical Technology Beijing Institute of TechnologyBeijing100081China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai200031China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University&Institute of Semiconductors Chinese Academy of SciencesLanzhou730000China

The article(Zheng et al.,2024)unfortunately contained a mistake:in Table 3,four"I"elements in the last"LF in thalamus"column are *** four arrows should be"↑"in the following correctversi... 详细信息

关键词： connectivity mistake dynamic

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Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

arXiv

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arXiv 2022年

作者： Chen, Huayu He, Huanhuan Zhu, Jing Sun, Shuting Li, Jianxiu Shao, Xuexiao Li, Junxiang Li, Xiaowei Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China Brain Health Engineering Laboratory School of Medical Technology Beijing Institute of Technology Beijing China School of Information Science and Engineering Inner Mongolia University Inner Mongolia China Shandong Academy of Intelligent Computing Technology Jinan China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Open Source Software and Real-Time System Lanzhou University Ministry of Education Lanzhou China

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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