A review on software and hardware developments in automatic epilepsy diagnosis using<scp>EEG</scp>datasets

Handa, Palak; Gupta, Ekta; Muskan, Sheik; Goel, Nidhi

doi:10.1111/exsy.13374

Cited by 6 publications

(1 citation statement)

References 227 publications

(233 reference statements)

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“…Secured, reproducible AI algorithms, good quality data, and efficient computing horse power are the major elements for the development of early detection, and prediction of epileptic waveforms through EEG signals [2,3]. There are several types of EEGs such as intracranial, scalp, ambulatory, etc through which good quality data can be generated.…”

Section: Introductionmentioning

confidence: 99%

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep learning methods. Good quality, open-source, and free EEG data acts as a catalyst in this ongoing battle to manage this disease. This article presents 40+ publicly available EEG datasets for adult and pediatric human populations from 2001-2023. A comparative analysis and discussion on open and private EEG datasets have been done based on objective parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used for the automatic detection of epileptic and seizure EEG signals. Meta-data has also been released for large EEG data like CHB-MIT. This article will be updated every year to report the progress and changing trends in the development of EEG datasets in this field.

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Section: Introductionmentioning

confidence: 99%

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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Detecting Seizures from EEG Data: The Potential of Deep Learning Techniques

Mamta,

Mane,

Vyas

et al. 2023

2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS)

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DFEF: Diversify feature enhancement and fusion for online knowledge distillation

Liang,

Zhang,

Liu

et al. 2024

Expert Systems

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Traditional knowledge distillation relies on high‐capacity teacher models to supervise the training of compact student networks. To avoid the computational resource costs associated with pretraining high‐capacity teacher models, teacher‐free online knowledge distillation methods have achieved satisfactory performance. Among these methods, feature fusion methods have effectively alleviated the limitations of training without the strong guidance of a powerful teacher model. However, existing feature fusion methods often focus primarily on end‐layer features, overlooking the efficient utilization of holistic knowledge loops and high‐level information within the network. In this article, we propose a new feature fusion‐based mutual learning method called Diversify Feature Enhancement and Fusion for Online Knowledge Distillation (DFEF). First, we enhance advanced semantic information by mapping multiple end‐of‐network features to obtain richer feature representations. Next, we design a self‐distillation module to strengthen knowledge interactions between the deep and shallow network layers. Additionally, we employ attention mechanisms to provide deeper and more diversified enhancements to the input feature maps of the self‐distillation module, allowing the entire network architecture to acquire a broader range of knowledge. Finally, we employ feature fusion to merge the enhanced features and generate a high‐performance virtual teacher to guide the training of the student model. Extensive evaluations on the CIFAR‐10, CIFAR‐100, and CINIC‐10 datasets demonstrate that our proposed method can significantly enhance performance compared to state‐of‐the‐art feature fusion‐based online knowledge distillation methods. Our code can be found at https://github.com/JSJ515-Group/DFEF-Liu.

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A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

Cited by 6 publications

References 227 publications

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

Detecting Seizures from EEG Data: The Potential of Deep Learning Techniques

DFEF: Diversify feature enhancement and fusion for online knowledge distillation

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