  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-modal 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.
