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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

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