Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity

Li, Zhengdao; Hwang, Kai; Li, Keqin; Wu, Jie; Ji, Te

doi:10.1038/s41598-022-23656-1

Cited by 15 publications

(6 citation statements)

References 57 publications

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“…As mentioned in the earlier section, the forward and backward propagation all together contribute to this efficiency. [32][33][34] The difference in terms of rate of accuracy is significantly observable, which in all cases of the specimens is more than 2%. Finally, in Table 3, comparison of the performance designed work is made with respect to three other competent schemes.…”

Section: Experimental Analysismentioning

confidence: 90%

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Speeded up robust features trailed GCN for seizure identification during pregnancy

Nayak,

Padhy,

Mishra

2024

JIST

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from the MRI. Second, these features are further mapped to a graph convolutional neural network (GCN). The maximal clique is generated out of these intermediate features and subjected to convolutional neural network (CNN) architecture for classification. The maximal clique acts as an efficient tool for representing final and fine-tuned feature points through combined graph convolution and thus contributes towards efficient classification. The designed framework is validated through benchmark dataset images presented by NITRC. Experimental evaluation is made on samples of 'male', 'female' and 'female with pregnancy'. The overall rate of accuracy stands at 96%, 95%, and 95% respectively.

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Section: Experimental Analysismentioning

confidence: 90%

“…Deep graph neural networks (GNNs) for seizure prediction from MRI data is introduced. 32 They model brain connectivity using GNNs to enhance prediction accuracy. The study reported a prediction accuracy of 94%.…”

Section: Literature Reviewmentioning

confidence: 99%

Speeded up robust features trailed GCN for seizure identification during pregnancy

Nayak,

Padhy,

Mishra

2024

JIST

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“…Adversaries possess the capability to launch attacks on the autopilot system of autonomous vehicles by attaching stickers to traffic signs. The fact that most patch attack methods do not consider the issue of determining the ideal spot in an image to inject the patch is a significant restriction [115]. Current patch attack methods either learn patches that are universal across locations or use a fixed place as the patch site.…”

Section: A Patch Attacksmentioning

confidence: 99%

Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

Mehta,

Padaria,

Bavisi

et al. 2024

IEEE Access

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Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks.VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections. INDEX TERMS ADAS, Advanced Driver Assistance Systems, Attacks, Countermeasures, Defences, Security, Threats I. INTRODUCTION ADAS stands for "Advanced Driver Assistance System". It describes a group of electronic devices and technologies that help drivers operate their cars more safely and effectively. ADAS has numerous electrical and digital tools that aid drivers in minimizing errors and performing parking functions, among other things. The primary goal of this study is to lessen human mistakes and prevent accidents through technologies like automatic emergency braking, collision avoidance, lane-keeping assistance, and autonomous cruise

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“…Most epilepsy diagnosis studies use EEG data. Li et al [158] proposed a structuregenerated GNN model for learning the spatial-temporal dynamic features of EEG signals. Tao et al [101] constructed dynamic brain networks from EEG and used a GIN model to predict seizure.…”

Section: Epilepsymentioning

confidence: 99%

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

Zhang,

Yang,

Zhang

et al. 2023

Brain Sciences

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Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.

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Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity

Cited by 15 publications

References 57 publications

Speeded up robust features trailed GCN for seizure identification during pregnancy

Speeded up robust features trailed GCN for seizure identification during pregnancy

Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

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