Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Lanzhou University Second Hospital, Lanzhou, China.
Sci Data. 2022 Apr 19;9(1):178. doi: 10.1038/s41597-022-01211-x.
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. 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. We present a multi-modal open dataset for mental-disorder analysis. 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. 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 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.
根据世界卫生组织的数据，精神障碍患者数量，尤其是抑郁症患者数量不断增加，已成为全球疾病负担的主要因素之一。随着人工智能等工具的兴起，利用生理数据探索精神障碍的新的可能生理指标，并为精神障碍诊断创造新的应用，已成为一个新的研究热点。我们提出了一个用于精神障碍分析的多模态开放数据集。该数据集包括来自临床抑郁症患者和匹配正常对照组的脑电图（EEG）和口语记录数据，这些患者均由医院的专业精神科医生精心诊断和选择。EEG 数据集包括使用传统的 128 电极弹性帽和用于普及计算应用的可穿戴 3 电极 EEG 采集器收集的数据。53 名参与者的 128 电极 EEG 信号分别在静息状态和执行点探测任务时进行了记录；55 名参与者的 3 电极 EEG 信号在静息状态下进行了记录；52 名参与者的音频数据在访谈、阅读和图片描述期间进行了记录。
