同步经颅磁刺激-脑电图(transcranial magnetic stimulation-electroencephalography,TMS-EEG)是一种将经颅磁刺激与脑电记录同步整合的技术。一方面,EEG能够记录TMS脉冲引起的瞬时神经电生理反应,另一方面,TMS脉冲的施加也能基于所记录...同步经颅磁刺激-脑电图(transcranial magnetic stimulation-electroencephalography,TMS-EEG)是一种将经颅磁刺激与脑电记录同步整合的技术。一方面,EEG能够记录TMS脉冲引起的瞬时神经电生理反应,另一方面,TMS脉冲的施加也能基于所记录的EEG信号来进行状态依赖的精准调控。本文结合这两个特点提出并系统梳理了同步TMS-EEG在心理学研究中的三种主要应用模式:神经生理评估、因果性揭示神经机制以及大脑闭环调控。文章将围绕这三条主线,区分并比较不同模式在工作机制、实验方案与应用目标上的差异,并结合近10年的心理学相关研究,梳理各模式已有研究的主要发现,以期为应用同步TMS-EEG技术提供清晰的理论框架与实践指南。展开更多
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to...Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to friction pain.The results showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex(PFC).The activation area decreased,and the negative activation intensity in the PFC region increased with increasing intensity of pain.The inhibitory interactions between different brain regions,especially between the PFC and primary somatosensory cortex(SI)regions were enhanced,and excitatory-inhibitory connections between the medial and lateral pain pathways were balanced during pain perception.The percentage power spectral density of theαrhythm(Dα),dominant singularity strength(αpeak)and longest vertical line(Vmax)of EEG signals induced by pain significantly decreased,and the percent-age power spectral density of theβrhythm(Dβ)significantly increased.The combination of multiple features of Dα,Dβ,αpeak and Vmax could significantly improve the average recognition accuracy of different pain states.This study elucidated the neural processing mechanisms of friction-induced pain,and EEG features associated with friction pain were extracted and recognized.It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-Computer Interface(BCI)system related to pain.展开更多
Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(...Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.展开更多
Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-ti...Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.展开更多
文摘同步经颅磁刺激-脑电图(transcranial magnetic stimulation-electroencephalography,TMS-EEG)是一种将经颅磁刺激与脑电记录同步整合的技术。一方面,EEG能够记录TMS脉冲引起的瞬时神经电生理反应,另一方面,TMS脉冲的施加也能基于所记录的EEG信号来进行状态依赖的精准调控。本文结合这两个特点提出并系统梳理了同步TMS-EEG在心理学研究中的三种主要应用模式:神经生理评估、因果性揭示神经机制以及大脑闭环调控。文章将围绕这三条主线,区分并比较不同模式在工作机制、实验方案与应用目标上的差异,并结合近10年的心理学相关研究,梳理各模式已有研究的主要发现,以期为应用同步TMS-EEG技术提供清晰的理论框架与实践指南。
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
基金National Natural Science Foundation of China(grant number:52375224)Natural Science Foundation of Jiangsu Province(grant number:BK20242086)+2 种基金Priority Academic Program Development of Jiangsu Higher Education Institutions,a project supported by"the Fundamental Research Funds for the Central Universities"(grant number:202410976)Graduate Innovation Program of China University of Mining and Technology(grant number:2024WLKXJ075)Postgraduate Research&Practice Innovation Program of Jiangsu Province(grant number:KYCX24_2719).
文摘Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to friction pain.The results showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex(PFC).The activation area decreased,and the negative activation intensity in the PFC region increased with increasing intensity of pain.The inhibitory interactions between different brain regions,especially between the PFC and primary somatosensory cortex(SI)regions were enhanced,and excitatory-inhibitory connections between the medial and lateral pain pathways were balanced during pain perception.The percentage power spectral density of theαrhythm(Dα),dominant singularity strength(αpeak)and longest vertical line(Vmax)of EEG signals induced by pain significantly decreased,and the percent-age power spectral density of theβrhythm(Dβ)significantly increased.The combination of multiple features of Dα,Dβ,αpeak and Vmax could significantly improve the average recognition accuracy of different pain states.This study elucidated the neural processing mechanisms of friction-induced pain,and EEG features associated with friction pain were extracted and recognized.It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-Computer Interface(BCI)system related to pain.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.
基金supported in part by the National Natural Science Foundation of China(Grant No.82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)of Shenzhen Science and Technology Innovation Committee+6 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Natural Science Foundation of Jiangsu Province(No.BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038 and SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575)the Henan Province Science and Technology Research(222102310322)The Jiangsu Students’Innovation and Entrepreneurship Training Program(202110304096Y).
文摘Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.
