摘要：
Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCI EEG datasets ( N=192) and an emotion recognition EEG dataset ( N=45), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.
期刊：
Journal of Autism and Developmental Disorders,2023年53(6):2314-2327 ISSN：0162-3257
通讯作者：
Jingying Chen
作者机构：
[Chen, Xianke; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.;[Chen, Xianke; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Liao, Mengyi] Pingdingshan Univ, Coll Comp Sci & Technol, Pingdingshan 467000, Henan, Peoples R China.;[Wang, Guangshuai] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.
通讯机构：
[Jingying Chen] N;National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, People’s Republic of China
期刊：
Artificial Intelligence in Medicine,2023年145:102677 ISSN：0933-3657
通讯作者：
Jiang, XP
作者机构：
[Fu, Chengcheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Jiang, Xingpeng; Fu, Chengcheng; He, Tingting] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Fu, Chengcheng; van Harmelen, Frank; Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands.;[Fu, Chengcheng; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Natl Language Resources Monitor Res Ctr Network Me, Wuhan, Peoples R China.;[Huang, Zhisheng] Tongji Univ, Sch Med, Clin Res Ctr Mental Disorders, Shanghai Pudong New Area Mental Hlth Ctr, Shanghai, Peoples R China.
通讯机构：
[Jiang, XP ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
关键词：
Food;Gut microbiota;Knowledge graph;Mental health
摘要：
Sustained attention is one of the basic abilities of humans to maintain concentration on relevant information while ignoring irrelevant information over extended periods. The purpose of the review is to provide insight into how to integrate neural mechanisms of sustained attention with computational models to facilitate research and application. Although many studies have assessed attention, the evaluation of humans' sustained attention is not sufficiently comprehensive. Hence, this study provides a current review on both neural mechanisms and computational models of visual sustained attention. We first review models, measurements, and neural mechanisms of sustained attention and propose plausible neural pathways for visual sustained attention. Next, we analyze and compare the different computational models of sustained attention that the previous reviews have not systematically summarized. We then provide computational models for automatically detecting vigilance states and evaluation of sustained attention. Finally, we outline possible future trends in the research field of sustained attention.
作者机构：
[Cai, Chang] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.;[Gao, Yijing; Nagarajan, Srikantan S.; Cai, Chang; Hinkley, Leighton] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA.;[Hashemi, Ali; Haufe, Stefan] Charite Univ Med Berlin, Berlin Ctr Adv Neuroimaging, Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Elect Engn & Comp Sci Fac, Machine Learning Grp, Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Inst Math, Berlin, Germany.
通讯机构：
[Cai, C.] N;[Nagarajan, S.S.] D;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China<&wdkj&>Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States
关键词：
Dynamics of neural activity;Brain source power changes;Five-dimensional neuroimaging;Electromagnetic brain imaging;Bayesian inference
摘要：
Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction of high-resolution (HR) EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG SR framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding SR EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution (LR) EEG and reconstructs the corresponding HR acquisitions through an end-to-end SR course. The experimental results on three EEG datasets (autism spectrum disorder, emotion, and motor imagery) indicate that: 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with normalized mean squared error (NMSE) decreased by <inline-formula> <tex-math notation="LaTeX">$1\%$</tex-math> </inline-formula>–<inline-formula> <tex-math notation="LaTeX">$6\%$</tex-math> </inline-formula> and the improvement of signal-to-noise ratio (SNR) up to <inline-formula> <tex-math notation="LaTeX">$1.2$</tex-math> </inline-formula> dB and 2) the SR EEG manifests superiority to the LR alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of SR. IEEE
摘要：
Disease-causing genes prioritization is very important to understand disease mechanisms and biomedical applications, such as design of drugs. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease genes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network while perform poorly when they are applied to loosely connected disease genes. To address these problems, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases. The experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study three representative diseases, Meckel syndrome 1, Protein C deficiency and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that certain complex disease-causing genes can be divided into several modules that are closely associated with different disease phenotype.
期刊：
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS,2015年13(4):378-394 ISSN：1748-5673
通讯作者：
He, Tingting
作者机构：
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Shen, Xianjun; Yuan, Jie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[He, Tingting] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
摘要：
In this paper, we develop a novel regularisation method for MVAR via weighted fusion which considers the correlation among variables. In theory, we discuss the grouping effect of weighted fusion regularisation for linear models. By virtue of the probability method, we show that coefficients corresponding to highly correlated predictors have small differences. A quantitative estimate for such small differences is given regardless of the coefficients signs. The estimate is also improved when consider empirical approximation error if the model fit the data well. We then apply the proposed model on several time series data sets especially a time series dataset of human gut microbiomes. The experimental results indicate that the new approach has better performance than several other VAR-based models and we also demonstrate its capability of extracting relevant microbial interactions.
期刊：
IEEE Transactions on NanoBioscience,2014年13(2):80-88 ISSN：1536-1241
通讯作者：
Zhao, Junmin
作者机构：
[Zhao, Junmin] Henan Univ Urban Construct, Pingdingshan 467036, Peoples R China.;[He, Tingting; Shen, Xianjun; Hu, Xiaohua; Li, Peng; Zhang, Ming] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Zhao, Junmin] Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Zhao, Junmin] N;Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
关键词：
Biological network;gene co-express;protein complex;weighted PPI network
摘要：
Recent studies have shown that protein complex is composed of core proteins and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge (PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate, P-value between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.
期刊：
IEEE Transactions on NanoBioscience,2014年13(2):89-96 ISSN：1536-1241
通讯作者：
Li, Peng
作者机构：
[He, Tingting; Shen, Xianjun; Hu, Xiaohua; Li, Peng; Zhang, Ming; Wang, Yan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Zhao, Junmin] Henan Univ Urban Construct, Pingdingshan 467036, Peoples R China.
通讯机构：
[Li, Peng] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
关键词：
Algorithm CACE;connected affinity;effective and accurately;overlapping functional modules
摘要：
A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity which is inferred from protein complexes is interpreted as the reliability and possibility of interaction. The protein interaction network is constructed as a weighted graph, and the weight is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effectively and accurately when compared with other state-of-art algorithms CPM and IPC-MCE.
