Background Frontal lobe injury(FLI)is related to cognitive control impairments,but the influences of FLI on the internal subprocesses of cognitive control remain unclear.Aims We sought to identify specific biomarkers ...Background Frontal lobe injury(FLI)is related to cognitive control impairments,but the influences of FLI on the internal subprocesses of cognitive control remain unclear.Aims We sought to identify specific biomarkers for long-term dysfunction or compensatory modulation in different cognitive control subprocesses.Methods A retrospective case-control study was conducted.Event-related potentials(ERP),oscillations and functional connectivity were used to analyse electroencephalography(EEG)data from 12 patients with unilateral frontal lobe injury(UFLI),12 patients with bilateral frontal lobe injury(BFLI)and 26 healthy controls(HCs)during a Go/NoGo task,which included several subprocesses:perceptual processing,anticipatory preparation,conflict monitoring and response decision.Results Compared with the HC group,N2(the second negative peak in the averaged ERP waveform)latency,and frontal and parietal oscillations were decreased only in the BFLI group,whereas P3(the third positive peak in the averaged ERP waveform)amplitudes and sensorimotor oscillations were decreased in both patient groups.The functional connectivity of the four subprocesses was as follows:alpha connections of posterior networks in the BFLI group were lower than in the HC and UFLI groups,and these alpha connections were negatively correlated with neuropsychological tests.Theta connections of the dorsal frontoparietal network in the bilateral hemispheres of the BFLI group were lower than in the HC and UFLI groups,and these connections in the uninjured hemisphere of the UFLI group were higher than in the HC group,which were negatively correlated with behavioural performances.Delta and theta connections of the midfrontal-related networks in the BFLI group were lower than in the HC group.Theta across-network connections in the HC group were higher than in the BFLI group but lower than in the UFLI group.Conclusions The enhancement of low-frequency connections reflects compensatory mechanisms.In contrast,alpha connections are the opposite,therefore revealing more abnormal neural activity and less compensatory connectivity as the severity of injury increases.The nodes of the above networks may serve as stimulating targets for early treatment to restore corresponding functions.EEG biomarkers can measure neuromodulation effects in heterogeneous patients.展开更多
Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling.Recent study proposed t...Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling.Recent study proposed to solve the limited label problem via domain adaptation methods.However,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this study.The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'outputs.Besides,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed.Finally,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is constructed.Extensive experiments on SEED and SEED‐IV are conducted.The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.展开更多
Fatigue is a state commonly caused by overworked,which seriously affects daily work and life.How to detect mental fatigue has always been a hot spot for researchers to explore.Electroencephalogram(EEG)is considered on...Fatigue is a state commonly caused by overworked,which seriously affects daily work and life.How to detect mental fatigue has always been a hot spot for researchers to explore.Electroencephalogram(EEG)is considered one of the most accurate and objective indicators.This article investigated the devel-opment of classification algorithms applied in EEG-based fatigue detection in recent years.According to the different source of the data,we can divide these classification algorithms into two categories,intra-subject(within the same sub-ject)and cross-subject(across different subjects).In most studies,traditional machine learning algorithms with artificial feature extraction methods were com-monly used for fatigue detection as intra-subject algorithms.Besides,deep learn-ing algorithms have been applied to fatigue detection and could achieve effective result based on large-scale dataset.However,it is difficult to perform long-term calibration training on the subjects in practical applications.With the lack of large samples,transfer learning algorithms as a cross-subject algorithm could promote the practical application of fatigue detection methods.We found that the research based on deep learning and transfer learning has gradually increased in recent years.But as afield with increasing requirements,researchers still need to con-tinue to explore efficient decoding algorithms,design effective experimental para-digms,and collect and accumulate valid standard data,to achieve fast and accurate fatigue detection methods or systems to further widely apply.展开更多
There exists various neurological disorder based diseases like tumor,sleep disorder,headache,dementia and Epilepsy.Among these,epilepsy is the most common neurological illness in humans,comparable to stroke.Epilepsy i...There exists various neurological disorder based diseases like tumor,sleep disorder,headache,dementia and Epilepsy.Among these,epilepsy is the most common neurological illness in humans,comparable to stroke.Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging(MRI).Neurons are intricately coupled in order to communicate and generate signals from human organs.Due to the complex nature of electroencephalogram(EEG)signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task.Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis.Their performancemay sometime degrade due to missing information,selection of poor classification model and unavailability of quality data that are used to train the models for better prediction.This research work is an attempt to epileptic seizures detection by using amulti focus dataset based on EEG signals and brainMRI.The key steps of this work are:feature extraction having two different streams i.e.,EEGusingwavelet transformation along with SVD-Entropy,and MRI using convolutional neural network(CNN),after extracting features fromboth streams,feature fusion is applied to generate feature vector used by support vector machine(SVM)to diagnose the epileptic seizures.From the experimental evaluation and results comparison with the current state-of-the-art techniques,it has been concluded that the performance of the proposed scheme is better than the existing models.展开更多
Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important rese...Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.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
The term Epilepsy refers to a most commonly occurring brain disorder after a migraine.Early identification of incoming seizures significantly impacts the lives of people with Epilepsy.Automated detection of epileptic ...The term Epilepsy refers to a most commonly occurring brain disorder after a migraine.Early identification of incoming seizures significantly impacts the lives of people with Epilepsy.Automated detection of epileptic seizures(ES)has dramatically improved the life quality of the patients.Recent Electroencephalogram(EEG)related seizure detection mechanisms encountered several difficulties in real-time.The EEGs are the non-stationary signal,and seizure patternswould changewith patients and recording sessions.Further,EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs.Artificial intelligence(AI)methods in the domain of ES analysis use traditional deep learning(DL),and machine learning(ML)approaches.This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection(OAOFS-DBNECD)technique using EEG signals.The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs.The suggested OAOFS-DBNECD technique transforms the EEG signals into.csv format at the initial stage.Next,the OAOFS technique selects an optimal subset of features using the preprocessed data.For seizure classification,the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer(AEO)with a deep belief network(DBN)model.An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm.The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies.In addition,the result of the suggested approach has been evaluated using the CHB-MIT database,and the findings demonstrate accuracy of 97.81%.These findings confirmed the best seizure categorization accuracy on the EEG data considered.展开更多
基金This work was supported by the National Natural Science Foundation of China(82271933,81971800 and 81871536)Priority Academic Program Development of Jiangsu Higher Education Institutes(PAPD)(SYSD2012063)Shandong Provincial Natural Science Foundation(ZR2023QH434),China.
文摘Background Frontal lobe injury(FLI)is related to cognitive control impairments,but the influences of FLI on the internal subprocesses of cognitive control remain unclear.Aims We sought to identify specific biomarkers for long-term dysfunction or compensatory modulation in different cognitive control subprocesses.Methods A retrospective case-control study was conducted.Event-related potentials(ERP),oscillations and functional connectivity were used to analyse electroencephalography(EEG)data from 12 patients with unilateral frontal lobe injury(UFLI),12 patients with bilateral frontal lobe injury(BFLI)and 26 healthy controls(HCs)during a Go/NoGo task,which included several subprocesses:perceptual processing,anticipatory preparation,conflict monitoring and response decision.Results Compared with the HC group,N2(the second negative peak in the averaged ERP waveform)latency,and frontal and parietal oscillations were decreased only in the BFLI group,whereas P3(the third positive peak in the averaged ERP waveform)amplitudes and sensorimotor oscillations were decreased in both patient groups.The functional connectivity of the four subprocesses was as follows:alpha connections of posterior networks in the BFLI group were lower than in the HC and UFLI groups,and these alpha connections were negatively correlated with neuropsychological tests.Theta connections of the dorsal frontoparietal network in the bilateral hemispheres of the BFLI group were lower than in the HC and UFLI groups,and these connections in the uninjured hemisphere of the UFLI group were higher than in the HC group,which were negatively correlated with behavioural performances.Delta and theta connections of the midfrontal-related networks in the BFLI group were lower than in the HC group.Theta across-network connections in the HC group were higher than in the BFLI group but lower than in the UFLI group.Conclusions The enhancement of low-frequency connections reflects compensatory mechanisms.In contrast,alpha connections are the opposite,therefore revealing more abnormal neural activity and less compensatory connectivity as the severity of injury increases.The nodes of the above networks may serve as stimulating targets for early treatment to restore corresponding functions.EEG biomarkers can measure neuromodulation effects in heterogeneous patients.
基金supported in part by the National Natural Science Foundation of China under Grants 61866039in part by the Natural Science Foundation of Chongqing,China(No.cstc2019jscxmbdxX0021)+1 种基金in part by the Excellent Youths Project for Basic Research of Yunnan Province(No.202101AW070015)in part by the Key Cooperation Project of Chongqing Municipal Education Commission(No.HZ2021008).
文摘Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling.Recent study proposed to solve the limited label problem via domain adaptation methods.However,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this study.The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'outputs.Besides,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed.Finally,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is constructed.Extensive experiments on SEED and SEED‐IV are conducted.The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.
基金funded by the National Natural Science Foundation of China(Grant Nos.61906019,62006082 and 62076103)the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2021A1515011853,2021A1515011600 and 2020A1515110294)+1 种基金Guangzhou Science and Technology Plan Project(Grant No.202102020877)the Guangzhou Science and Technology Plan Project Key Field R&D Project(202007030005).
文摘Fatigue is a state commonly caused by overworked,which seriously affects daily work and life.How to detect mental fatigue has always been a hot spot for researchers to explore.Electroencephalogram(EEG)is considered one of the most accurate and objective indicators.This article investigated the devel-opment of classification algorithms applied in EEG-based fatigue detection in recent years.According to the different source of the data,we can divide these classification algorithms into two categories,intra-subject(within the same sub-ject)and cross-subject(across different subjects).In most studies,traditional machine learning algorithms with artificial feature extraction methods were com-monly used for fatigue detection as intra-subject algorithms.Besides,deep learn-ing algorithms have been applied to fatigue detection and could achieve effective result based on large-scale dataset.However,it is difficult to perform long-term calibration training on the subjects in practical applications.With the lack of large samples,transfer learning algorithms as a cross-subject algorithm could promote the practical application of fatigue detection methods.We found that the research based on deep learning and transfer learning has gradually increased in recent years.But as afield with increasing requirements,researchers still need to con-tinue to explore efficient decoding algorithms,design effective experimental para-digms,and collect and accumulate valid standard data,to achieve fast and accurate fatigue detection methods or systems to further widely apply.
基金the Deanship of Scientific Research at King Saud University for funding this work through research group No(RG-1439-023).
文摘There exists various neurological disorder based diseases like tumor,sleep disorder,headache,dementia and Epilepsy.Among these,epilepsy is the most common neurological illness in humans,comparable to stroke.Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging(MRI).Neurons are intricately coupled in order to communicate and generate signals from human organs.Due to the complex nature of electroencephalogram(EEG)signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task.Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis.Their performancemay sometime degrade due to missing information,selection of poor classification model and unavailability of quality data that are used to train the models for better prediction.This research work is an attempt to epileptic seizures detection by using amulti focus dataset based on EEG signals and brainMRI.The key steps of this work are:feature extraction having two different streams i.e.,EEGusingwavelet transformation along with SVD-Entropy,and MRI using convolutional neural network(CNN),after extracting features fromboth streams,feature fusion is applied to generate feature vector used by support vector machine(SVM)to diagnose the epileptic seizures.From the experimental evaluation and results comparison with the current state-of-the-art techniques,it has been concluded that the performance of the proposed scheme is better than the existing models.
基金Project supported by the Xuzhou Key Research and Development Program (Social Development) (Grant No. KC21304)the National Natural Science Foundation of China (Grant No. 61876186)。
文摘Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.
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.
文摘The term Epilepsy refers to a most commonly occurring brain disorder after a migraine.Early identification of incoming seizures significantly impacts the lives of people with Epilepsy.Automated detection of epileptic seizures(ES)has dramatically improved the life quality of the patients.Recent Electroencephalogram(EEG)related seizure detection mechanisms encountered several difficulties in real-time.The EEGs are the non-stationary signal,and seizure patternswould changewith patients and recording sessions.Further,EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs.Artificial intelligence(AI)methods in the domain of ES analysis use traditional deep learning(DL),and machine learning(ML)approaches.This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection(OAOFS-DBNECD)technique using EEG signals.The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs.The suggested OAOFS-DBNECD technique transforms the EEG signals into.csv format at the initial stage.Next,the OAOFS technique selects an optimal subset of features using the preprocessed data.For seizure classification,the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer(AEO)with a deep belief network(DBN)model.An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm.The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies.In addition,the result of the suggested approach has been evaluated using the CHB-MIT database,and the findings demonstrate accuracy of 97.81%.These findings confirmed the best seizure categorization accuracy on the EEG data considered.
