The applied principles of EEG analysis methods in neuroscience and clinical neurology

H Zhang, QQ Zhou, H Chen, XQ Hu, WG Li, Y Bai… - Military Medical …, 2023 - Springer
Electroencephalography (EEG) is a non-invasive measurement method for brain activity.
Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural …

D2PAM: Epileptic seizures prediction using adversarial deep dual patch attention mechanism

AA Khan, RK Madendran… - CAAI Transactions …, 2023 - Wiley Online Library
Epilepsy is considered as a serious brain disorder in which patients frequently experience
seizures. The seizures are defined as the unexpected electrical changes in brain neural …

[HTML][HTML] StressNet: Hybrid model of LSTM and CNN for stress detection from electroencephalogram signal (EEG)

SAM Mane, A Shinde - Results in Control and Optimization, 2023 - Elsevier
Everyday tasks can cause stress, which can lead to serious medical conditions, such as
depression. EEG signal processing can assist medical professionals in managing the …

Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: a systematic review

N Moutonnet, S White, BP Campbell, D Mandic… - arXiv preprint arXiv …, 2024 - arxiv.org
Machine learning algorithms for seizure detection have shown great diagnostic potential,
with recent reported accuracies reaching 100%. However, few published algorithms have …

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

MH Alshayeji - Machine Learning: Science and Technology, 2023 - iopscience.iop.org
Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person's
way of life. Manual diagnosis is a laborious and time-consuming task prone to human error …

A multi representation deep learning approach for epileptic seizure detection

AT Hermawan, IAE Zaeni, AP Wibawa… - Journal of Robotics …, 2024 - journal.umy.ac.id
Epileptic seizures, unpredictable in nature and potentially dangerous during activities like
driving, pose significant risks to individual and public safety. Traditional diagnostic methods …

Novel deep learning framework for detection of epileptic seizures using EEG signals

S Mallick, V Baths - Frontiers in Computational Neuroscience, 2024 - frontiersin.org
Introduction Epilepsy is a chronic neurological disorder characterized by abnormal electrical
activity in the brain, often leading to recurrent seizures. With 50 million people worldwide …

Epileptic seizure prediction using 1D-MobileNet

SJ Shilpa, R Mehta, S Vityazev… - 2023 25th International …, 2023 - ieeexplore.ieee.org
Automatic seizure prediction is helpful to Epilepsy patients to identify the possibility of
seizure events momentarily. This could mitigate the risk faced by them while driving …

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

H Ahuja, S Badhwar, H Edgell, M Litoiu… - Frontiers in Human …, 2024 - frontiersin.org
The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms
known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as …

Integrated TSVM-TSK fusion for enhanced EEG-based epileptic seizure detection: Robust classifier with competitive learning

C Kalpana, G Mohanbabu - Biomedical Signal Processing and Control, 2024 - Elsevier
Early diagnosis of epilepsy is crucial for patient survival and well-being, making it essential
to develop effective methods for early disease detection based on health parameters. This …