Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall h...Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall health. However, there have been no studies on the effects of implant treatment on electroencephalography (EEG). In this study, we investigated the effects of restoration of masticatory function by implant treatment on EEG and stress. Methods: 13 subjects (6 males, 7 females, age 64.1 ± 5.8 years) who had lost masticatory function due to tooth loss and 11 healthy subjects (6 males, 5 females, age 47.6 ± 2.4 years) as a control group. EEG (θ, α, β waves, α/β ratio) and salivary cortisol were measured before immediate dental implant treatment and every month of treatment for 6 months. EEG (θ, α, β waves, α/β ratio) was measured with a simple electroencephalograph miniature DAQ terminal (Intercross-410, Intercross Co., Ltd., Japan) in a resting closed-eye condition, and salivary cortisol was measured using an ELISA kit. Results: Compared to the control group, the appearance of θ and α waves were significantly decreased and β waves were increased, and α/β ratio was significantly decreased. The cortisol level of the subject group was significantly higher compared with the control group. With the course of implant treatment, the appearance of θ and α waves of the subject group increased, while β waves decreased. However, no significant difference was observed. The α/β ratio of the subject group increased from the first month after implant treatment and increased significantly after 5 and 6 months (0 vs. 5 months: p < 0.05, 0 vs. 6 months: p < 0.01). The cortisol levels in the subject group decreased from the first month after implant treatment and significantly decreased after 3 or 4 months (0 vs. 3 months: p < 0.05, 0 vs. 4 months: p < 0.01). These results suggest that tooth loss causes mental stress, which decreases brain stimulation and affects function. Restoration of masticatory function by implants was suggested to alleviate the effects on brain function and stress.展开更多
Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and h...Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and health,but is challenging due to the unique characteristics of EEG and lack of standardized protocols.This study aims to develop and compare 18 machine learning models using Automated Machine Learning(autoML)technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification.The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning.The data is obtained from neonates at post-menstrual age 37±05 weeks.352530-s EEG segments from 19 infants are used to train and test the proposed models.There are twelve time and frequency domain features extracted from each channel.Each model receives the common features of nine channels as an input vector of size 108.Each model’s performance was evaluated based on a variety of evaluation metrics.The maximum mean accuracy of 84.78%and kappa of 69.63%has been obtained by the AutoML-based Random Forest estimator.This is the highest accuracy for EEG-based sleep-wake classification,until now.While,for the AutoML-based Adaboost Random Forest model,accuracy and kappa were 84.59%and 69.24%,respectively.High performance achieved in the proposed autoML-based approach can facilitate early identification and treatment of sleep-related issues in neonates.展开更多
Electroencephalography(EEG)analysis extracts critical information from brain signals,enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces.However,performing an artificial i...Electroencephalography(EEG)analysis extracts critical information from brain signals,enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces.However,performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices,especially with large neural network models.Herein,we propose an EEG opto-processor based on diffractive photonic computing units(DPUs)to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures.The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification,which monitors the brain state to identify symptoms of an epileptic seizure.We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets,that is,the Children’s Hospital Boston(CHB)–Massachusetts Institute of Technology(MIT)extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets,with excellent computing performance results.Along with the channel selection mechanism,both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis.Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.展开更多
The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAH...The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.展开更多
文摘Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall health. However, there have been no studies on the effects of implant treatment on electroencephalography (EEG). In this study, we investigated the effects of restoration of masticatory function by implant treatment on EEG and stress. Methods: 13 subjects (6 males, 7 females, age 64.1 ± 5.8 years) who had lost masticatory function due to tooth loss and 11 healthy subjects (6 males, 5 females, age 47.6 ± 2.4 years) as a control group. EEG (θ, α, β waves, α/β ratio) and salivary cortisol were measured before immediate dental implant treatment and every month of treatment for 6 months. EEG (θ, α, β waves, α/β ratio) was measured with a simple electroencephalograph miniature DAQ terminal (Intercross-410, Intercross Co., Ltd., Japan) in a resting closed-eye condition, and salivary cortisol was measured using an ELISA kit. Results: Compared to the control group, the appearance of θ and α waves were significantly decreased and β waves were increased, and α/β ratio was significantly decreased. The cortisol level of the subject group was significantly higher compared with the control group. With the course of implant treatment, the appearance of θ and α waves of the subject group increased, while β waves decreased. However, no significant difference was observed. The α/β ratio of the subject group increased from the first month after implant treatment and increased significantly after 5 and 6 months (0 vs. 5 months: p < 0.05, 0 vs. 6 months: p < 0.01). The cortisol levels in the subject group decreased from the first month after implant treatment and significantly decreased after 3 or 4 months (0 vs. 3 months: p < 0.05, 0 vs. 4 months: p < 0.01). These results suggest that tooth loss causes mental stress, which decreases brain stimulation and affects function. Restoration of masticatory function by implants was suggested to alleviate the effects on brain function and stress.
文摘Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and health,but is challenging due to the unique characteristics of EEG and lack of standardized protocols.This study aims to develop and compare 18 machine learning models using Automated Machine Learning(autoML)technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification.The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning.The data is obtained from neonates at post-menstrual age 37±05 weeks.352530-s EEG segments from 19 infants are used to train and test the proposed models.There are twelve time and frequency domain features extracted from each channel.Each model receives the common features of nine channels as an input vector of size 108.Each model’s performance was evaluated based on a variety of evaluation metrics.The maximum mean accuracy of 84.78%and kappa of 69.63%has been obtained by the AutoML-based Random Forest estimator.This is the highest accuracy for EEG-based sleep-wake classification,until now.While,for the AutoML-based Adaboost Random Forest model,accuracy and kappa were 84.59%and 69.24%,respectively.High performance achieved in the proposed autoML-based approach can facilitate early identification and treatment of sleep-related issues in neonates.
基金supported by the National Major Science and Technology Projects of China(2021ZD0109902 and 2020AA0105500)the National Natural Science Fundation of China(62275139 and 62088102)the Tsinghua University Initiative Scientific Research Program.
文摘Electroencephalography(EEG)analysis extracts critical information from brain signals,enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces.However,performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices,especially with large neural network models.Herein,we propose an EEG opto-processor based on diffractive photonic computing units(DPUs)to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures.The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification,which monitors the brain state to identify symptoms of an epileptic seizure.We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets,that is,the Children’s Hospital Boston(CHB)–Massachusetts Institute of Technology(MIT)extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets,with excellent computing performance results.Along with the channel selection mechanism,both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis.Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.
基金funded by National Nature Science Foundation of China,Yunnan Funda-Mental Research Projects,Special Project of Guangdong Province in Key Fields of Ordinary Colleges and Universities and Chaozhou Science and Technology Plan Project of Funder Grant Numbers 82060329,202201AT070108,2023ZDZX2038 and 202201GY01.
文摘The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.
