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
Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications(ASMs),a condition known as pharmacoresistant epilepsy.The management of pharmacoresistant epilepsy remains an intract...Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications(ASMs),a condition known as pharmacoresistant epilepsy.The management of pharmacoresistant epilepsy remains an intractable issue in the clinic.Its early prediction is important for prevention and diagnosis.However,it still lacks effective predictors and approaches.Here,a classical model of pharmacoresistant temporal lobe epilepsy(TLE)was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats.Ictal electroencephalograms(EEGs)recorded before phenytoin treatment were analyzed.Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats,a convolutional neural network predictive model was constructed to predict pharmacoresistance,and achieved 78% prediction accuracy.We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power,which was verified in seizure EEGs from pharmacoresistant TLE patients.Prospectively,therapies targeting the subiculum in those predicted as“pharmacoresistant”individual rats significantly reduced the subsequent occurrence of pharmacoresistance.These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model.This may be of translational importance for the precise management of pharmacoresistant TLE.展开更多
A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI system...A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.展开更多
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.
基金supported by grants from the National Key R&D Program of China(2020YFA0803900)the National Natural Science Foundation of China(82173796 and U21A20418)+1 种基金the Natural Science Foundation of Zhejiang Province(LD22H310003)the Key R&D Plan of Zhejiang Province(2021C03116).
文摘Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications(ASMs),a condition known as pharmacoresistant epilepsy.The management of pharmacoresistant epilepsy remains an intractable issue in the clinic.Its early prediction is important for prevention and diagnosis.However,it still lacks effective predictors and approaches.Here,a classical model of pharmacoresistant temporal lobe epilepsy(TLE)was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats.Ictal electroencephalograms(EEGs)recorded before phenytoin treatment were analyzed.Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats,a convolutional neural network predictive model was constructed to predict pharmacoresistance,and achieved 78% prediction accuracy.We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power,which was verified in seizure EEGs from pharmacoresistant TLE patients.Prospectively,therapies targeting the subiculum in those predicted as“pharmacoresistant”individual rats significantly reduced the subsequent occurrence of pharmacoresistance.These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model.This may be of translational importance for the precise management of pharmacoresistant TLE.
基金supported by the Natural Science Foundation of Hebei Province(F2024202019)the National Natural Science Foundation of China(32201072).
文摘A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.
