![]() ![]() 6 used high temporal resolution of EEG to compare the spectral properties of resting-state functional connectivity in individuals with a major depressive disorder to healthy controls. 5 focused on analyzing EEG alpha frequency and suggested burnout was associated with alpha power, whereas depression was linked to individual alpha frequency. 4 found frontal EEG asymmetry may serve as a biomarker of depression risk. And it was discovered that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. 3 proposed a technique that can learn automatically from the input EEG signals to differentiate EEGs obtained from depressive and normal participants. EEG trades for a higher temporal resolution at the cost of low spatial resolution. ![]() These reasons are causing the global population to still be widely untreated for their mental health disorders.Īs non-invasive physiological data, Electroencephalography (EEG) provides a direct measure of postsynaptic potentials with millisecond temporal resolution. The result of depression diagnosis is also not as convincing as some other illnesses, such as hypertension, due to its lack of physiological indicators. The process is not only labor-consuming but also time-consuming. However, diagnosis of depression is currently based on interviews, and clinical scales carried out by professionals, such as psychiatrists and psychologists. Thus, major depressive disorder (MDD) has become a leading contributor to the global burden of disease. According to the WHO 2015 statistics, the total estimated number of global diagnosed depression patients reached 322 million 1 and increased by 18.4% between 20 2. The 128-electrodes EEG signals of 53 participants were recorded as both in resting state and while doing the Dot probe tasks the 3-electrode EEG signals of 55 participants were recorded in resting-state the audio data of 52 participants were recorded during interviewing, reading, and picture description.įor the past decades, the number of mental disorder patients, especially depression patients, has overgrown. The EEG dataset includes data collected using a traditional 128-electrodes mounted elastic cap and a wearable 3-electrode EEG collector for pervasive computing applications. The dataset includes EEG and recordings of spoken language data from clinically depressed patients and matching normal controls, who were carefully diagnosed and selected by professional psychiatrists in hospitals. We present a multi-modal open dataset for mental-disorder analysis. With the rising of tools such as 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. According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. ![]()
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