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
Closed-loop neuromodulation,especially using the phase of the electroencephalography(EEG)rhythm to assess the real-time brain state and optimize the brain stimulation process,is becoming a hot research topic.Because t...Closed-loop neuromodulation,especially using the phase of the electroencephalography(EEG)rhythm to assess the real-time brain state and optimize the brain stimulation process,is becoming a hot research topic.Because the EEG signal is non-stationary,the commonly used EEG phase-based prediction methods have large variances,which may reduce the accuracy of the phase prediction.In this study,we proposed a machine learning-based EEG phase prediction network,which we call EEG phase prediction network(EPN),to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data.We verified the performance of EPN on pre-recorded data,simulated EEG data,and a real-time experiment.Compared with widely used state-of-the-art models(optimized multi-layer filter architecture,auto-regress,and educated temporal prediction),EPN achieved the lowest variance and the greatest accuracy.Thus,the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.展开更多
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
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 by the Key Collaborative Research Program of the Alliance of International Science Organizations(ANSO-CR-KP-2022-10)Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200200)+2 种基金Natural Science Foundation of China(82151307,82202253,and 31620103905)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32030207)Science Frontier Program of the Chinese Academy of Sciences(QYZDJ-SSW-SMCO19).
文摘Closed-loop neuromodulation,especially using the phase of the electroencephalography(EEG)rhythm to assess the real-time brain state and optimize the brain stimulation process,is becoming a hot research topic.Because the EEG signal is non-stationary,the commonly used EEG phase-based prediction methods have large variances,which may reduce the accuracy of the phase prediction.In this study,we proposed a machine learning-based EEG phase prediction network,which we call EEG phase prediction network(EPN),to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data.We verified the performance of EPN on pre-recorded data,simulated EEG data,and a real-time experiment.Compared with widely used state-of-the-art models(optimized multi-layer filter architecture,auto-regress,and educated temporal prediction),EPN achieved the lowest variance and the greatest accuracy.Thus,the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
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。
