MODMA dataset: a Multi-model Open Dataset for Mental-disorder Analysis

02/20/2020
by   Hanshu Cai, et al.
0

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-model open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis.

READ FULL TEXT

page 5

page 7

page 10

research
02/20/2020

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorde...
research
09/28/2020

Neural Networks based approaches for Major Depressive Disorder and Bipolar Disorder Diagnosis using EEG signals: A review

Mental disorders represent critical public health challenges as they are...
research
10/07/2021

EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia

Mental disorders are among the leading causes of disability worldwide. T...
research
05/21/2016

Automatic Detection of Epileptiform Discharges in the EEG

The diagnosis of epilepsy generally includes a visual inspection of EEG ...
research
07/06/2023

Brain Computer Interface (BCI) based on Electroencephalographic (EEG) patterns due to new cognitive tasks

New mental tasks were investigated for suitability in Brain-Computer Int...
research
08/07/2022

Bias Reducing Multitask Learning on Mental Health Prediction

There has been an increase in research in developing machine learning mo...
research
10/14/2019

Wearables and location tracking technologies for mental-state sensing in outdoor environments

Advances in commercial wearable devices are increasingly facilitating th...

Please sign up or login with your details

Forgot password? Click here to reset