BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals DOI Creative Commons

shraddha jain

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Дек. 20, 2023

Abstract BrainWave Diagnostics, an emerging field, leverages electroencephalography (EEG) data for cost-effective and resource-efficient neurological disorder detection. Although EEGs are commonly used disease detection, their low signal intensity nonlinear features pose analytical challenges. This review explores the use of high-performance computational tools, machine learning, deep learning methods in diagnosing a range disorders, including epilepsy, Parkinson's disease, autism, ADHD, stroke, tumors, schizophrenia, Alzheimer's, depression, alcohol disorder. The increasing prevalence disorders resource burden underscores urgency these diagnostic advancements. Future research can consider multi-modal approaches, providing practical solutions detection beyond EEGs, with potential applications diverse analysis domains.

Язык: Английский

From phenomenological to biophysical models of seizures DOI Creative Commons
Damien Depannemaecker,

Aitakin Ezzati,

Huifang Wang

и другие.

Neurobiology of Disease, Год журнала: 2023, Номер 182, С. 106131 - 106131

Опубликована: Апрель 21, 2023

Epilepsy is a complex disease that requires various approaches for its study. This short review discusses the contribution of theoretical and computational models. The presents frameworks underlie understanding certain seizure properties their classification based on dynamical at onset offset seizures. Dynamical system tools are valuable resources in study These can provide insights into mechanisms offer framework classification, by analyzing complex, dynamic behavior Additionally, models have high potential clinical applications, as they be used to develop more accurate diagnostic personalized medicine tools. We discuss modeling span different scales levels, while also questioning neurocentric view, emphasizing importance considering glial cells. Finally, we explore epistemic value provided this type approach.

Язык: Английский

Процитировано

14

Editorial: What AI and Neuroscience Can Learn from Each Other—Open Problems in Models and Theories DOI
Asim Roy, Ali A. Minai, Jean‐Philippe Thivierge

и другие.

Cognitive Computation, Год журнала: 2024, Номер 16(5), С. 2331 - 2333

Опубликована: Июль 23, 2024

Язык: Английский

Процитировано

0

Would you publish unrealistic model? DOI
Damien Depannemaecker

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Авг. 29, 2024

Theoretical neurosciences research community produces many models of different natures to capture activities or functions the brain.Some these are some presented as "realistic" models, often because variable and parameters have biophysical units, but not always.In this short technical spotlight, I explain why term can be misleading propose elements that useful characterize a model.

Язык: Английский

Процитировано

0

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals DOI Creative Commons

shraddha jain

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Дек. 20, 2023

Abstract BrainWave Diagnostics, an emerging field, leverages electroencephalography (EEG) data for cost-effective and resource-efficient neurological disorder detection. Although EEGs are commonly used disease detection, their low signal intensity nonlinear features pose analytical challenges. This review explores the use of high-performance computational tools, machine learning, deep learning methods in diagnosing a range disorders, including epilepsy, Parkinson's disease, autism, ADHD, stroke, tumors, schizophrenia, Alzheimer's, depression, alcohol disorder. The increasing prevalence disorders resource burden underscores urgency these diagnostic advancements. Future research can consider multi-modal approaches, providing practical solutions detection beyond EEGs, with potential applications diverse analysis domains.

Язык: Английский

Процитировано

0