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Neonatal White Matter Damage Analysis Using DTI Super-Resolution and Multi-Modality Image Registration
Int J Neural Syst. 2023 Nov 17:2450001. doi: 10.1142/S0129065724500011. Online ahead of print.ABSTRACTPunctate White Matter Damage (PWMD) is a common neonatal brain disease, which can easily cause neurological disorder and strongly affect life quality in terms of neuromotor and cognitive performance. Especially, at the neonatal stage, the best cure time can be easily missed because PWMD is not conducive to the diagnosis based on current existing methods. The lesion of PWMD is relatively straightforward on T1-weighted Magnetic Resonance Imaging (T1 MRI), showing semi-oval, cluster or linear high signals. Diffusion Tensor Ma...
Source: International Journal of Neural Systems - November 20, 2023 Category: Neurology Authors: Yi Wang Yuan Zhang Chi Ma Rui Wang Zhe Guo Yu Shen Miaomiao Wang Hongying Meng Source Type: research

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection
Int J Neural Syst. 2023 Nov 15:2450003. doi: 10.1142/S0129065724500035. Online ahead of print.ABSTRACTTo avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different level...
Source: International Journal of Neural Systems - November 15, 2023 Category: Neurology Authors: Xinyuan Chen Yi Niu Yanna Zhao Xue Qin Source Type: research

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection
Int J Neural Syst. 2023 Nov 15:2450003. doi: 10.1142/S0129065724500035. Online ahead of print.ABSTRACTTo avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different level...
Source: International Journal of Neural Systems - November 15, 2023 Category: Neurology Authors: Xinyuan Chen Yi Niu Yanna Zhao Xue Qin Source Type: research

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection
Int J Neural Syst. 2023 Nov 15:2450003. doi: 10.1142/S0129065724500035. Online ahead of print.ABSTRACTTo avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different level...
Source: International Journal of Neural Systems - November 15, 2023 Category: Neurology Authors: Xinyuan Chen Yi Niu Yanna Zhao Xue Qin Source Type: research

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection
Int J Neural Syst. 2023 Nov 15:2450003. doi: 10.1142/S0129065724500035. Online ahead of print.ABSTRACTTo avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different level...
Source: International Journal of Neural Systems - November 15, 2023 Category: Neurology Authors: Xinyuan Chen Yi Niu Yanna Zhao Xue Qin Source Type: research

Hybrid Network for Patient-Specific Seizure Prediction from EEG Data
Int J Neural Syst. 2023 Nov;33(11):2350056. doi: 10.1142/S0129065723500569.ABSTRACTSeizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN bas...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Yongfeng Zhang Tiantian Xiao Ziwei Wang Hongbin Lv Shuai Wang Hailing Feng Shanshan Zhao Yanna Zhao Source Type: research

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals
Int J Neural Syst. 2023 Nov;33(11):2350055. doi: 10.1142/S0129065723500557.ABSTRACTAutomated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Shuangyong Zhang Hong Wang Zixi Zheng Tianyu Liu Weixin Li Zishan Zhang Yanshen Sun Source Type: research

Hybrid Network for Patient-Specific Seizure Prediction from EEG Data
Int J Neural Syst. 2023 Nov;33(11):2350056. doi: 10.1142/S0129065723500569.ABSTRACTSeizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN bas...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Yongfeng Zhang Tiantian Xiao Ziwei Wang Hongbin Lv Shuai Wang Hailing Feng Shanshan Zhao Yanna Zhao Source Type: research

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals
Int J Neural Syst. 2023 Nov;33(11):2350055. doi: 10.1142/S0129065723500557.ABSTRACTAutomated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Shuangyong Zhang Hong Wang Zixi Zheng Tianyu Liu Weixin Li Zishan Zhang Yanshen Sun Source Type: research

Hybrid Network for Patient-Specific Seizure Prediction from EEG Data
Int J Neural Syst. 2023 Nov;33(11):2350056. doi: 10.1142/S0129065723500569.ABSTRACTSeizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN bas...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Yongfeng Zhang Tiantian Xiao Ziwei Wang Hongbin Lv Shuai Wang Hailing Feng Shanshan Zhao Yanna Zhao Source Type: research

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals
Int J Neural Syst. 2023 Nov;33(11):2350055. doi: 10.1142/S0129065723500557.ABSTRACTAutomated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Shuangyong Zhang Hong Wang Zixi Zheng Tianyu Liu Weixin Li Zishan Zhang Yanshen Sun Source Type: research

Hybrid Network for Patient-Specific Seizure Prediction from EEG Data
Int J Neural Syst. 2023 Nov;33(11):2350056. doi: 10.1142/S0129065723500569.ABSTRACTSeizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN bas...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Yongfeng Zhang Tiantian Xiao Ziwei Wang Hongbin Lv Shuai Wang Hailing Feng Shanshan Zhao Yanna Zhao Source Type: research

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals
Int J Neural Syst. 2023 Nov;33(11):2350055. doi: 10.1142/S0129065723500557.ABSTRACTAutomated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Shuangyong Zhang Hong Wang Zixi Zheng Tianyu Liu Weixin Li Zishan Zhang Yanshen Sun Source Type: research

Hybrid Network for Patient-Specific Seizure Prediction from EEG Data
Int J Neural Syst. 2023 Nov;33(11):2350056. doi: 10.1142/S0129065723500569.ABSTRACTSeizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN bas...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Yongfeng Zhang Tiantian Xiao Ziwei Wang Hongbin Lv Shuai Wang Hailing Feng Shanshan Zhao Yanna Zhao Source Type: research

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals
Int J Neural Syst. 2023 Nov;33(11):2350055. doi: 10.1142/S0129065723500557.ABSTRACTAutomated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure...
Source: International Journal of Neural Systems - October 30, 2023 Category: Neurology Authors: Shuangyong Zhang Hong Wang Zixi Zheng Tianyu Liu Weixin Li Zishan Zhang Yanshen Sun Source Type: research