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
Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,M...Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,Member of the Epilepsy Research Centre Prague-EpiReC Consortium,Prague,Czechia"should only be assigned to Radek Janca and Petr Jezdik.It is removed from the authors:Jiri Hammer,Michaela Kajsova,Adam Kalina,Petr Marusic,and Kamil Vlcek.展开更多
Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Metho...Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.展开更多
EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses.However,the high dimensionality of EEG signals and their continuous variability in the time-freque...EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses.However,the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging.Therefore,advanced deep learning methods are needed to extract meaningful features and improve classification performance.This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network(TCN)mechanisms for EEG-based emotion recognition.EEG signals are first converted into scalogram images using Continuous Wavelet Transform(CWT),and classification is performed on these images.Swin Transformer is used to extract spatial features in scalogram images,and the TCN method is used to learn long-term dependencies.In addition,attention mechanisms are integrated to highlight the essential features extracted from both models.The effectiveness of the proposed model has been tested on the SEED dataset,widely used in the field of emotion recognition,and it has consistently achieved high performance across all emotional classes,with accuracy,precision,recall,and F1-score values of 97.53%,97.54%,97.53%,and 97.54%,respectively.Compared to traditional transfer learning models,the proposed approach achieved an accuracy increase of 1.43%over ResNet-101,1.81%over DenseNet-201,and 2.44%over VGG-19.In addition,the proposed model outperformed many recent CNN,RNN,and Transformer-based methods reported in the literature.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.
文摘Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,Member of the Epilepsy Research Centre Prague-EpiReC Consortium,Prague,Czechia"should only be assigned to Radek Janca and Petr Jezdik.It is removed from the authors:Jiri Hammer,Michaela Kajsova,Adam Kalina,Petr Marusic,and Kamil Vlcek.
基金This study received financial support from the Jilin Province Health and Technology Capacity Enhancement Project(Project Number:222Lc132).
文摘Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.
文摘EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses.However,the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging.Therefore,advanced deep learning methods are needed to extract meaningful features and improve classification performance.This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network(TCN)mechanisms for EEG-based emotion recognition.EEG signals are first converted into scalogram images using Continuous Wavelet Transform(CWT),and classification is performed on these images.Swin Transformer is used to extract spatial features in scalogram images,and the TCN method is used to learn long-term dependencies.In addition,attention mechanisms are integrated to highlight the essential features extracted from both models.The effectiveness of the proposed model has been tested on the SEED dataset,widely used in the field of emotion recognition,and it has consistently achieved high performance across all emotional classes,with accuracy,precision,recall,and F1-score values of 97.53%,97.54%,97.53%,and 97.54%,respectively.Compared to traditional transfer learning models,the proposed approach achieved an accuracy increase of 1.43%over ResNet-101,1.81%over DenseNet-201,and 2.44%over VGG-19.In addition,the proposed model outperformed many recent CNN,RNN,and Transformer-based methods reported in the literature.
