Parent Mark, Albuquerque Isabela, Tiwari Abhishek, Cassani Raymundo, Gagnon Jean-François, Lafond Daniel, Tremblay Sébastien, Falk Tiago H
INRS-EMT, Université du Québec, Montréal, QC, Canada.
Thales Research and Technology Canada, Quebec City, QC, Canada.
Front Neurosci. 2020 Dec 8;14:542934. doi: 10.3389/fnins.2020.542934. eCollection 2020.
With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.
随着可穿戴设备以及被动式身体/脑机接口（B/BCI）的迅速发展，日常环境中的自动化压力监测最近受到了广泛关注，其应用范围涵盖从严肃游戏到临床监测等多个领域。然而，对于移动用户而言，由于其他重叠的（且可能造成混淆的）生理反应（例如由于身体活动引起的）可能会掩盖压力的影响，以及在测量信号中可能引入的运动伪迹，从而带来了挑战。例如，传统的心率增加不再能仅仅归因于压力，也可能是由活动本身引起的。这使得移动被动式B/BCI的开发具有挑战性。在本文中，我们介绍了PASS，这是一个从48名参与者收集的身体活动和压力的多模态数据库。参与者在三种不同的活动水平下执行了不同压力水平的任务，并提供了他们感知到的压力和疲劳水平的定量评级。为了控制压力，使用了两款视频游戏（即一款平静的探索游戏和一款生存游戏）。在整个实验过程中测量了外周身体活动（心电图、皮肤电活动、呼吸、皮肤温度）以及大脑活动（脑电图）。提供了实验方案的完整描述，并进行了初步分析，以研究在存在身体活动的情况下对压力的生理反应。PASS数据库，包括原始数据和主观评级，已在http://musaelab.ca/pass-database/上向研究社区开放。希望这个数据库将有助于推进移动被动式B/BCI在日常环境中的应用。
