In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5-89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main ...
We present a dataset combining electrophysiology and eye tracking intended as a resource for the inv...
The interview is still the main and most important tool in psychiatrist's work. The neuroimaging met...
Mental disorders represent critical public health challenges as they are leading contributors to the...
In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to...
Most brain related databases bring together specialized information, with a growing number that incl...
Innovations in methods and technologies are equipping researchers with unprecedented capabilities fo...
According to the World Health Organisation, the number of mental disorder patients, especially depre...
We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disor...
Mental, neurological, and neurodevelopmental (MNN) disorders impose an enormous burden of disease gl...
Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of tre...
University of Minnesota Ph.D. dissertation. 2016. Major: Electrical Engineering. Advisor: Keshab Par...
University of Technology Sydney. Faculty of Engineering and Information Technology.Major depressive ...
The proposed research develops new computational tools to identify, diagnose, and predict treatment ...
Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canon...
The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in...
We present a dataset combining electrophysiology and eye tracking intended as a resource for the inv...
The interview is still the main and most important tool in psychiatrist's work. The neuroimaging met...
Mental disorders represent critical public health challenges as they are leading contributors to the...
In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to...
Most brain related databases bring together specialized information, with a growing number that incl...
Innovations in methods and technologies are equipping researchers with unprecedented capabilities fo...
According to the World Health Organisation, the number of mental disorder patients, especially depre...
We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disor...
Mental, neurological, and neurodevelopmental (MNN) disorders impose an enormous burden of disease gl...
Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of tre...
University of Minnesota Ph.D. dissertation. 2016. Major: Electrical Engineering. Advisor: Keshab Par...
University of Technology Sydney. Faculty of Engineering and Information Technology.Major depressive ...
The proposed research develops new computational tools to identify, diagnose, and predict treatment ...
Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canon...
The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in...
We present a dataset combining electrophysiology and eye tracking intended as a resource for the inv...
The interview is still the main and most important tool in psychiatrist's work. The neuroimaging met...
Mental disorders represent critical public health challenges as they are leading contributors to the...