邬霞
博士 教授 博士生导师
北京师范大学 人工智能学院
主要研究方向为人工智能与脑科学 ,致力于运用人工智能技术理论和方法,挖掘人脑特征探索认知功能的脑机制,同时受益于脑科学探究中获得的启发与灵感,开发新的人工智能理论方法
个性化签名
- 姓名:邬霞
- 目前身份:在职研究人员
- 担任导师情况:博士生导师
- 学位:博士
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学术头衔:
博士生导师
- 职称:高级-教授
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学科领域:
人工智能
- 研究兴趣:主要研究方向为人工智能与脑科学 ,致力于运用人工智能技术理论和方法,挖掘人脑特征探索认知功能的脑机制,同时受益于脑科学探究中获得的启发与灵感,开发新的人工智能理论方法
邬霞,北京师范大学人工智能学院教授,认知神经科学与学习国家重点实验室研究员,博士生导师。
教育背景:
2005年—2007年 北京师范大学认知神经科学与学习研究所 基础心理学 博士
2001年-2004年 北京师范大学信息科学学院 通信与信息系统专业 硕士
1997年-2001年 北京师范大学信息科学学院电子系 通信与信息系统专业 学士
工作经历:
2019年-至今 北京师范大学人工智能学院 教授
2014年-2019年 北京师范大学信息科学与技术学院 教授
2011年-2014年 北京师范大学信息科学与技术学院 副教授
2008年-2011年 北京师范大学信息科学与技术学院 讲师
2004年-2005年 北京师范大学认知神经科学与学习研究所 助理研究员
研究领域:
主要研究方向为人工智能与脑科学 ,致力于运用人工智能技术理论和方法,挖掘人脑特征探索认知功能的脑机制,同时受益于脑科学探究中获得的启发与灵感,开发新的人工智能理论方法
获奖情况:
2020年,吴文俊人工智能科学技术奖自然科学 一等奖
2020年,教育部高等学校科学研究优秀成果奖 一等奖
2014年,京师英才 一等奖
2012年,国家自然科学基金委优秀青年基金项目支持
2011年,入选教育部新世纪优秀人才支持计划
2010年,全国百篇优秀博士学位论文提名奖
科研成果:
在基于脑成像技术的大脑认知功能探究、神经反馈,脑 疾病诊断及预测等相关方面,以第一/通讯作者在Human Brain Mapping、NeuroImage、Pattern Recognition、Journal of Neural Engineering等高水平期刊,以及IPMI、MICCAI等医学图 像处理顶级国际会议发表论文60余篇。
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主页访问
249
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关注数
0
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成果阅读
1492
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成果数
12
【期刊论文】Multi-feature kernel discriminant dictionary learning for face recognition
Pattern Recognition,2017,66():404-411
2017年06月01日
The current study put forward a multi-feature kernel discriminant dictionary learning algorithm for face recognition. It was based on the supervised within-class-similar discriminative dictionary learning algorithm (SCDDL) we introduced previously. The proposed new algorithm was thus named as multi-feature kernel SCDDL (MKSCDDL). In contrast to the weighted combination or the constraint of representation coefficients for the feature combination used by some popular methods, MKSCDDL introduced the multiple kernel learning technique into the dictionary learning scheme. The experimental results on three large well-known face databases suggested that combination multiple features in MKSCDDL improved the recognition rate compared with SCDDL. In addition, adopting multiple kernel learning technique resulted in an excellent multi-feature dictionary learning approach when compared with some state-of-the-art multi-feature algorithms such as multiple kernel learning and multi-task joint sparse representation methods, indicating the effectiveness of the multiple kernel learning technique in the combination of multiple features for classification.
Multi-feature kernel discriminative dictionary learning Face recognition Multiple kernel learning
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NeuroImage,2018,173():258-274
2018年06月01日
The emotional Stroop task (EST) is among the most influential paradigms used to probe attention-related or cognitive control-related emotional processing in healthy subjects and clinical populations. The neuropsychological mechanism underlying the emotional Stroop effect has attracted extensive and long-lasting attention in both cognitive and clinical psychology and neuroscience; however, a precise characterization of the neural substrates underlying the EST in healthy and clinical populations remains elusive. Here, we implemented a coordinate-based meta-analysis covering functional imaging studies that employed the emotion-word or emotional counting Stroop paradigms to determine the underlying neural networks in healthy subjects and the trans-diagnostic alterations across clinical populations. Forty-six publications were identified that reported relevant contrasts (negative > neutral; positive > neutral) for healthy or clinical populations as well as for hyper- or hypo-activation of patients compared to controls. We demonstrate consistent involvement of the vlPFC and dmPFC in healthy subjects and consistent involvement of the vlPFC in patients. We further identify a trans-diagnostic pattern of hyper-activation in the prefrontal and parietal regions. These findings underscore the critical roles of cognitive control processes in the EST and implicate trans-diagnostic cognitive control deficits. Unlike the current models that emphasize the roles of the amygdala and rACC, our findings implicate novel mechanisms underlying the EST for both healthy and clinical populations.
Emotion-word Stroop Emotional counting Stroop Activation likelihood estimation fMRI Meta-analysis Cognitive control Emotional regulation
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J. Neural Eng.,2018,15(5):056014
2018年07月27日
Objective. Depression is a severe mental disorder. However, the neural mechanisms underlying affective interference (difficulties in directing attention away from negative distractors) in depression patients are still not well-understood. In particular, the connections between brain regions remain unclear. Using the emotional face-word Stroop task, we aimed to reveal the altered electroencephalography (EEG) functional connectivity in patients with depression, using concepts from event-related potentials (ERPs) and time series clustering. Approach. In this study, the EEG signals of ten healthy participants and ten depression patients were collected from a 64-sensor cap. Subsequently, EEG signals were segmented into temporal windows corresponding to the ERPs. For each duration, the dynamic time warping algorithm was used to calculate the similarities between EEG signals from different electrodes, and differences of these similarities were compared between the groups. Finally, hierarchical clustering was used to identify functionally connected regions and examine changes in depression. Main results. It was observed that during the time interval of 400–600 ms (N450 components), depression patients had more long-range connections than did healthy control patients and exhibited abnormal functional connectivity via the superior and middle frontal gyrus, specifically, the dorsolateral prefrontal cortex (DL-PFC, Brodmann's area 8 and 9), which is related to the control and resolution of affective interference. Moreover, the functionally connected region of depression patients was much larger than that of healthy participants, which is caused by brain resource reorganization. Significance. These findings thus provide new insights into the neural mechanisms of depression and further identify the DL-PFC and connections between certain electrodes as quantitative indicators of depression.
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Human Brain Mapping,2018,39(9):3701-3712
2018年05月10日
Narcissism is one of the most fundamental personality traits in which individuals in general population exhibit a large heterogeneity. Despite a surge of interest in examining behavioral characteristics of narcissism in the past decades, the neurobiological substrates underlying narcissism remain poorly understood. Here, we addressed this issue by applying a machine learning approach to decode trait narcissism from whole‐brain resting‐state functional connectivity (RSFC). Resting‐state functional MRI (fMRI) data were acquired for a large sample comprising 155 healthy adults, each of whom was assessed for trait narcissism. Using a linear prediction model, we examined the relationship between whole‐brain RSFC and trait narcissism. We demonstrated that the machine‐learning model was able to decode individual trait narcissism from RSFC across multiple neural systems, including functional connectivity between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes that contributed to the prediction model included the amygdala, prefrontal and anterior cingulate regions that have been linked to trait narcissism. These findings remained robust using different validation procedures. Our findings thus demonstrate that RSFC among multiple neural systems predicts trait narcissism at the individual level.
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【期刊论文】The Recognition of Multiple Anxiety Levels Based on Electroencephalograph
IEEE Transactions on Affective Computing,2019,(): 1 - 1
2019年08月20日
Anxiety is a complex emotional state that has a great impact on people's physical and mental health. Effectively identifying different anxiety states is very important. By inducing various anxiety states with electroencephalograph (EEG) recording, comprehensive EEG features (frequency and time domain features, statistical and nonlinear features) were extracted from different EEG bands and brain locations. Next, correlation analysis was performed for feature selection. And different classifiers were applied to classify four anxiety levels using different features alone or together to explore their anxiety recognition ability. Based on our dataset, the highest accuracy of identifying four anxiety states reached approximately 62.56% using the Support Vector Machine (SVM), which improved the classification accuracy compared with previous studies. The results also revealed the importance of EEG linear features (especially for features including total power, mean square and variance) in anxiety recognition. Furthermore, it suggested that EEG features in the beta band and the frontal lobe contributed to anxiety recognition more than the features in the other bands or other brain locations. In short, this study improves the accuracy of multi-level anxiety recognition and helps in choosing better features for anxiety recognition, which lay the foundation for the detection of continuous anxiety changes.
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IEEE Transactions on Affective Computing,2020,():1 - 1
2020年07月13日
In this paper, we investigate brain directed connectivity (BDC) networks for emotion recognition using electroencephalogram (EEG) source signals that were estimated from high-density sensor EEG signals, for the first time. Currently, a variety of features extracted from sensor EEG signals are used for emotion recognition. However, they cannot unambiguously describe the location of emotions associated with neural activities and information propagation or the interaction between brain regions. In addition, most current studies use low-density sensor EEG signals. Moreover, source signals estimated from high-density sensor EEG signal have not been employed for emotion recognition to date. We designed a BDC network-based framework using EEG source signals to investigate emotion recognition. The global cortex factor-based multivariate autoregressive (GCF-MVAR) method was utilized to extract emotion-related BDC features. Our study revealed that the combined BDC and DE features facilitated a recognition accuracy of up to 89.58 %, which is higher than the rate obtained from BDC features and DE features alone. The sensor features derived from high-density EEG signals also exhibited higher recognition accuracy compared to low-density EEG signals. These findings suggest that BDC features derived from EEG source signals can better characterize human emotional states and are meaningful for emotion recognition.
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Brain Structure and Function,2020,225():pages1587–
2020年04月24日
The connectivity hub property of the hippocampus (HIP) and the medial prefrontal cortex (MPFC) is essential for their widespread involvement in cognition; however, the cooperation mechanism between them is far from clear. Herein, using resting-state functional MRI and Gaussian Bayesian network to describe the directed organizing architecture of the HIP–MPFC pathway with regions in the brain, we demonstrated that the HIP and the MPFC have central roles as the driving hub and aggregating hub, respectively. The status of the HIP and the MPFC is dominant in communications between the HIP and the default-mode network, between the HIP and core neurocognitive networks, including the default-mode, frontoparietal, and salience networks, and between brain-wide representative regions, suggesting a strong and robust central position of the two regions in regulating the dynamics of large-scale brain activity. Furthermore, we found that the directed connectivity and flow from the right HIP to the MPFC is significantly linked to fluid intelligence. Together, these results clarify the different roles of the HIP and the MPFC that jointly contribute to network dynamics and cognitive ability from a data-driven insight via the use of the directed connectivity method.
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【期刊论文】Supervised Feature Selection With Orthogonal Regression and Feature Weighting
IEEE Transactions on Neural Networks and Learning Systems ,2020,(): 1 - 8
2020年05月14日
Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objective function can be solved by employing generalized power iteration and augmented Lagrangian multiplier methods. Experimental results show that the proposed method can more effectively reduce feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is also proved. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.
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IEEE/ACM Transactions on Computational Biology and Bioinformatics,2020,():1 - 1
2020年02月18日
Brain functional connectivity (FC) has shown great potential in becoming biomarkers of brain status. However, the problem of accurately estimating FC from complex-noisy fMRI time series remains unsolved. Usually, a regularization function is more appropriate in fitting the real inherent properties of the brain function activity pattern, which can further limit noise interference to improve the accuracy of the estimated result. Recently, the neuroscientists widely suggested that the inherent brain function activity pattern indicates sparse, modular and overlapping topology. However, previous studies have never considered this factual characteristic. Thus, we propose a novel method by the integration of these inherent brain function activity pattern priors to estimate FC. Extensive experiments on synthetic data demonstrate that our method can more accurately estimate the FC than previous. Then, we applied the estimated FC to predict the symptom severity of depressed patients, the symptom severity is related to subtle abnormal changes in the brain function activity, a more accurate FC can more effectively capture the subtle abnormal brain function activity changes. As results, our method better than others with a higher correlation coefficient of 0.4201. Moreover, the overlapping probabilistic of each brain region can be further explored by the proposed method.
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IEEE Journal of Biomedical and Health Informatics,2020,():1 - 1
2020年05月07日
Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder characterized by restricted interests and repetitive behaviors. Non-invasive measurements of brain activity with functional magnetic resonance imaging (fMRI) have demonstrated that the abnormality in the default mode network (DMN) is a crucial neural basis of ASD, but the time-frequency characteristic of the network has not yet been revealed. Hilbert-Huang transform (HHT) is conducive to feature extraction of biomedical signals and has recently been suggested as an effective method to explore the time-frequency feature of the brain activity and mechanism. In this study, the resting-state fMRI dataset of 105 subjects including 59 ASD participants and 46 healthy control (HC) participants were involved in the original time-frequency clustering analysis based on improved HHT and modified k-means clustering with label-replacement. Compared with HC, ASD selectively showed enhanced Hilbert Weight Frequency (HWF) in high frequency bands in crucial regions of the DMN, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC). Time-frequency clustering analysis revealed altered DMN organization in ASD. In the posterior DMN, the PCC and bilateral precuneus were separated for HC but clustered for ASD; in the anterior DMN, the clusters of ACC, dorsal MPFC, and ventral MPFC were relatively scattered for ASD. This study paves a promising way to uncover the alteration in the DMN of ASD and identify a potential neuroimaging biomarker for diagnostic reference.
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