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Feature-Selection-Based Transfer Learning for Intracortical Brain-Machine Interface Decoding

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 60-73页

作者： Zhang, Peng Li, Wei Ma, Xuan He, Jiping Huang, Jian Li, Qiang Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Northwestern Univ Dept Physiol Feinberg Sch Med Chicago IL 60611 USA Beijing Inst Technol Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs). Decoder calibration optimization approaches have been proposed, and neuron selection methods have been used to reduce computational burden. However, few methods can solve both problems simultaneously. In this article, we present a symmetrical-uncertainty-based transfer learning (SUTL) method that combines transfer learning with feature selection. The proposed method uses symmetrical uncertainty to quantitatively measure three indices for feature selection: stationarity, importance and redundancy of the feature. By selecting the stationary features, the disparities between the historical data and current data can be diminished, and the historical data can be effectively used for decoder calibration, thereby reducing the demand for current data. After selecting the important and non-redundant features, only the channels corresponding to them need to work;thus, the computational burden is reduced. The proposed method was tested on neural data recorded from two rhesus macaques to decode the reaching position or grasping gesture. The results showed that the SUTL method diminished the disparities between the historical data and current data, while achieving superior decoding performance with the needs of only ten current samples each category, less than 10% the number of features and 30% the number of neural recording channels. Additionally, unlike most studies on iBMIs, feature selection was implemented instead of neuron selection, and the average decoding accuracy achieved by the former was 6.6% higher.

关键词： Decoding Calibration Feature extraction Neurons Task analysis Brain-computer interfaces Measurement uncertainty Intracortical brain-machine interface decoder calibration transfer learning feature selection symmetrical uncertainty

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Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time Multi-person Eyeblink Detection in the Wild for Un...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zeng, Wenzheng Xiao, Yang Wei, Sicheng Gan, Jinfang Zhang, Xintao Cao, Zhiguo Fang, Zhiwen Zhou, Joey Tianyi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Southern Med Univ Sch Biomed Engn Guangzhou 510515 Peoples R China Southern Med Univ Zhujiang Hosp Dept Rehabil Med Guangzhou 510280 Peoples R China ASTAR Ctr Frontier Res Singapore Singapore ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9798350301298

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from unconstrained films to reveal "in the wild" characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counterparts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.

关键词： Video: Action and event understanding

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ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction

ADMEOOD: Out-of-Distribution Benchmark for Drug Property Pre...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wei, Shuoying Li, Songquan Guo, Yifei Zhu, Lida Wen, Xinlong Zhu, Rongbo College of Informatics Huazhong Agricultural University Wuhan430070 China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen518000 China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518000 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Hubei Wuhan430074 China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9798350337488

Obtaining accurate and effective information for drug molecules is a crucial and challenging task, which relies on high-quality chemical knowledge. However, chemical knowledge has been accumulated over the past 100 years from various regions, laboratories, and experimental purposes, which contains a lot of noise and inconsistency, leading to the out-of-distribution (OOD) problem. OOD may results in weak robustness and unsatisfied performance. In order to solve OOD learning problem with noise, a novel benchmark: ADMEOOD is proposed, which is a systematic OOD dataset curator and specifically designed for drug property prediction. ADMEOOD screens 27 Absorption, Distribution, Metabolism and Excretion (ADME) drug properties from Chembl and relevant literature. This paper explicitly make distinctions between two kinds of OOD data shifts: Noise Shift and Concept Conflict Drift (CCD). Overall, ADME contains 6 domain annotations combined with noise, CCD and no shifts, resulting in 18 different splits in total. ADMEOOD provides performance results on a variety of SOTA OOD models. The results demonstrate a significant difference performance between in-distribution and OOD data. Moreover, Empirical Risk Minimization and other models exhibit distinct trends in different domains and measurement types. The ADMEOOD benchmark can be accessed via https://***/qweasdzxc-wsy/ADMEOOD/. © 2023 IEEE.

关键词： ADME Benchmark Drug property prediction Out-of-distribution

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Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity

arXiv

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arXiv 2024年

作者： Shen, Tianqi Liu, Shaohua Feng, Jiaqi Ma, Ziye An, Ning Department of Computer Science City University of Hong Kong Hong Kong Image Processing Center Beihang University China Shen Yuan Honors College Beihang University China Research Institute of Mine Artificial Intelligence China Coal Research Institute China State Key Laboratory of Intelligent Coal Mining and Strata Control China

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

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Pattern phase transitions in 3D minimal Vicsek model

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EPL 2021年第5期134卷 50004-50004页

作者： Wu, Yue Li, Jiawei Chen, Duxin Li, Xiang Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Southeast Univ Jiangsu Key Lab Networked Collect Intelligence Sch Math Nanjing 210096 Peoples R China Fudan Univ Sch Informat Sci & Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Fudan Univ Sch Informat Sci & Engn Ctr Smart Networks & Syst Shanghai 200433 Peoples R China

A large part of biological collective behaviors involves motional formation patterns like migration, vortex, aggregation, etc., in three-dimensional space, but the phase transition principles among these patterns still remain a dilemma. In this letter, by tweaking the vision range of the particles in a minimal collective motion model, phase transitions are observed among gas-like, crystal-like, and liquid-sphere-like patterns. Significantly, the increase of noise magnitudes will destroy the moving direction synchronization of gas-like and crystal-like phases, but not the liquid-sphere-like phase. By contrast, three sub-phases of migration liquid-sphere, vortex liquid-sphere and aggregation liquid-sphere emerge by slightly increasing noise magnitudes. This study sheds some light onto the forming mechanisms of 3D motional patterns of abundant biological groups. These insights could be expected to enable tuning the collective motional formations of multi-unmanned systems or robots by simply tweaking a few dynamical parameters as well. Copyright (C) 2021 EPLA

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Federated Motor imagery Classification for Privacy-Preserving Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Jia, Tianwang Meng, Lubin Li, Siyang Liu, Jiajing Wu, Dongrui The Key Laboratory The Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University Science and Technology Research Institute Shenzhen518063 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy. © 2024, CC BY.

关键词： Federated learning

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State-of-Charge Dependent Battery Model Identification Using Gaussian Process Regression

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IFAC-PapersOnLine 2021年第3期54卷 647-652页

作者： Kesen Fan Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education China and School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China

This paper investigates the parameter identification of a state-of-charge dependent equivalent circuit model (ECM) for Lithium-ion batteries. Different from most existing ECM identification methods, we focus on identifying the functional relations between ECM parameters and state-of-charge (SOC). By transforming the ECM into an ARX model, a Gaussian process regression (GPR) approach is proposed, without using parametric functions to describe the SOC dependence of ARX coefficients. The proposed approach derives the posterior distributions of ECM parameters, thus is capable to quantify the estimation uncertainties. Another advantage lies in the flexibility of incorporating the knowledge of batteries into the prior distributions used in GPR, which enhances the estimation performance in the presence of noises. The effectiveness of the proposed GPR approach is illustrated by simulation examples under both low and high noise levels.

关键词： Gaussian process regression non-parametric approach Lithium-ion battery equivalent circuit model state-of-charge dependent circuit parameter

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Learning Probabilistic Coordinate Fields for Robust Correspondences

arXiv

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arXiv 2023年

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Lane Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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A2B: Anchor to Barycentric Coordinate for Robust Correspondence

arXiv

引用

arXiv 2023年

作者： Zhao, Weiyue Lu, Hao Cao, Zhiguo Li, Xin The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Lane Department of Computer Science and Electrical Engineering West Virginia University Commonwealth of Virginia Morgantown 15461 United States

There is a long-standing problem of repeated patterns in correspondence problems, where mismatches frequently occur because of inherent ambiguity. The unique position information associated with repeated patterns makes coordinate representations a useful supplement to appearance representations for improving feature correspondences. However, the issue of appropriate coordinate representation has remained unresolved. In this study, we demonstrate that geometric-invariant coordinate representations, such as barycentric coordinates, can significantly reduce mismatches between features. The first step is to establish a theoretical foundation for geometrically invariant coordinates. We present a seed matching and filtering network (SMFNet) that combines feature matching and consistency filtering with a coarse-to-fine matching strategy in order to acquire reliable sparse correspondences. We then introduce DEGREE, a novel anchor-to-barycentric (A2B) coordinate encoding approach, which generates multiple affine-invariant correspondence coordinates from paired images. DEGREE can be used as a plug-in with standard descriptors, feature matchers, and consistency filters to improve the matching quality. Extensive experiments in synthesized indoor and outdoor datasets demonstrate that DEGREE alleviates the problem of repeated patterns and helps achieve state-of-the-art performance. Furthermore, DEGREE also reports competitive performance in the third image Matching Challenge at CVPR 2021. This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences. Copyright © 2023, The Authors. All rights reserved.

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Intralayer Synchronization of Multiplex Dynamical Networks via Pinning Impulsive control

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第4期52卷 2110-2122页

作者： Liu, Hui Li, Jie Li, Zengyang Zeng, Zhigang Lu, Jinhu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China

These days, the synchronization of multiplex networks is an emerging and important research topic. Grounded framework and theory about synchronization and control on multiplex networks are yet to come. This article studies the intralayer synchronization on a multiplex network (i.e., a set of networks connected through interlayer edges), via the pinning impulsive control method. The topologies of different layers are independent of each other, and the individual dynamics of nodes in different layers are different as well. Supra-Laplacian matrices are adopted to represent the topological structures of multiplex networks. Two cases are considered according to impulsive sequences of multiplex networks: 1) pinning controllers are applied to all the layers simultaneously at the instants of a common impulse sequence and 2) pinning controllers are applied to each layer at the instants of distinct impulse sequences. Using the Lyapunov stability theory and the impulsive control theory, several intralayer synchronization criteria for multiplex networks are obtained, in terms of the supra-Laplacian matrix of network topology, self-dynamics of nodes, impulsive intervals, and the pinning control effect. Furthermore, the algorithms for implementing pinning schemes at every impulsive instant are proposed to support the obtained criteria. Finally, numerical examples are presented to demonstrate the effectiveness and correctness of the proposed schemes.

关键词： Multiplexing Synchronization Symmetric matrices Complex networks Couplings Linear matrix inequalities Intralayer synchronization multiplex network pinning impulsive control supra-Laplacian matrix

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