[论文解读] MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

Hanshu Cai, Yiwen Gao+24 more|arXiv (Cornell University)|Feb 20, 2020

EEG and Brain-Computer Interfaces参考文献 12被引用 56

一句话总结

引入 MODMA，一个用于精神障碍分析的多模态开放数据集，包含来自临床诊断的抑郁症患者及匹配对照的脑电 EEG 与音频数据。

ABSTRACT

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.

研究动机与目标

• 推动在精神障碍诊断中需要生理指标以补充访谈和临床量表。
• 提供一个高质量、公开可用的抑郁研究多模态数据集。
• 使能够测试和开发将脑电信号与音频信号耦合的精神障碍分析方法。
• 确保对受试者进行严格的临床诊断以提高数据集的可靠性。

提出的方法

• 使用传统的128电极帽和新型穿戴式3电极脑电记录仪收集 EEG 数据，以实现普及使用。
• 对53名受试者（128电极系统）在静息状态和刺激下记录 EEG。
• 对55名受试者记录静息态的3电极 EEG 数据。
• 在访谈、朗读和图片描述过程中捕捉52名受试者的音频数据。
• 确保所有受试者均由专业精神科医生诊断并有匹配对照。

实验结果

研究问题

• RQ1 相比单模态方法，多模态 EEG 与音频数据是否能提高抑郁症的检测或分析？
• RQ2 在临床诊断患者中，EEG 与音频信号中与抑郁相关的生理指标有哪些？
• RQ3 在多模态上，静息状态和刺激下的 EEG 模式在抑郁组与对照组之间有何差异？
• RQ4 在广泛应用的心理健康监测中部署3电极穿戴式 EEG 系统的可行性和价值如何？

主要发现

• 数据集包含53名受试者，使用128电极 EEG 在静息状态和刺激下记录。
• 数据集包含55名受试者，记录静息态的3电极 EEG。
• 音频数据可用于52名受试者，记录于访谈、朗读和图片描述期间。
• 所有受试者均在医院由专业精神科医生仔细诊断。
• 该数据集旨在使跨多模态信号的精神障碍分析方法测试成为可能。