International Journal of Neural Systems 
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This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.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
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers
Int J Neural Syst. 2023 Oct 20:2350065. doi: 10.1142/S012906572350065X. Online ahead of print.ABSTRACTReinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a f...
Source: International Journal of Neural Systems - October 19, 2023 Category: Neurology Authors: Enrique Adrian Villarrubia-Martin Luis Rodriguez-Benitez Luis Jimenez-Linares David Mu ñoz-Valero Jun Liu Source Type: research
Lightweight Seizure Detection Based on Multi-Scale Channel Attention
Int J Neural Syst. 2023 Oct 17:2350061. doi: 10.1142/S0129065723500612. Online ahead of print.ABSTRACTEpilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware...
Source: International Journal of Neural Systems - October 16, 2023 Category: Neurology Authors: Ziwei Wang Sujuan Hou Tiantian Xiao Yongfeng Zhang Hongbin Lv Jiacheng Li Shanshan Zhao Yanna Zhao Source Type: research
Lightweight Seizure Detection Based on Multi-Scale Channel Attention
Int J Neural Syst. 2023 Oct 17:2350061. doi: 10.1142/S0129065723500612. Online ahead of print.ABSTRACTEpilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware...
Source: International Journal of Neural Systems - October 16, 2023 Category: Neurology Authors: Ziwei Wang Sujuan Hou Tiantian Xiao Yongfeng Zhang Hongbin Lv Jiacheng Li Shanshan Zhao Yanna Zhao Source Type: research
Lightweight Seizure Detection Based on Multi-Scale Channel Attention
Int J Neural Syst. 2023 Oct 17:2350061. doi: 10.1142/S0129065723500612. Online ahead of print.ABSTRACTEpilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware...
Source: International Journal of Neural Systems - October 16, 2023 Category: Neurology Authors: Ziwei Wang Sujuan Hou Tiantian Xiao Yongfeng Zhang Hongbin Lv Jiacheng Li Shanshan Zhao Yanna Zhao Source Type: research
Announcement: The 2023 Hojjat Adeli Award for Outstanding Contributions in Neural Systems
Int J Neural Syst. 2023 Oct 13:2382001. doi: 10.1142/S0129065723820014. Online ahead of print.NO ABSTRACTPMID:37830299 | DOI:10.1142/S0129065723820014 (Source: International Journal of Neural Systems)
Source: International Journal of Neural Systems - October 13, 2023 Category: Neurology Source Type: research
Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach
Int J Neural Syst. 2023 Oct 13:2350064. doi: 10.1142/S0129065723500648. Online ahead of print.ABSTRACTConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which use...
Source: International Journal of Neural Systems - October 13, 2023 Category: Neurology Authors: Mosab A A Yousif Mahmut Ozturk Source Type: research
Announcement: The 2023 Hojjat Adeli Award for Outstanding Contributions in Neural Systems
Int J Neural Syst. 2023 Oct 13:2382001. doi: 10.1142/S0129065723820014. Online ahead of print.NO ABSTRACTPMID:37830299 | DOI:10.1142/S0129065723820014 (Source: International Journal of Neural Systems)
Source: International Journal of Neural Systems - October 13, 2023 Category: Neurology Source Type: research
