Task- and emotion-aware passive brain-computer interface for mental workload monitoring

用于心理负荷监测的任务和情绪感知被动脑机接口

• 批准号: RGPIN-2016-04210
• 负责人: Power, Sarah
• 金额: $ 2.26万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752813
• 关键词: Task; emotion; aware; passive; brain

Brain-computer interface (BCI) technologies have been traditionally defined as devices that enable movement-free communication through the direct measurement and translation of brain activity that is intentionally generated by the user for the purpose of controlling an external device. The target users of such “active” BCIs are generally individuals with severe motor impairments who are unable to operate more conventional interfaces (e.g., keyboards). Although this remains the focus of most BCI research, the potential of BCIs for healthy users has also been recognized and is receiving increased attention. The practical definition of BCIs is expanding to include not just those devices using brain activity to replace natural central nervous system (CNS) output in users with disability, but also those that enhance CNS output in healthy users. Rather than exploiting intentionally modulated brain activity, these so-called “passive BCIs” derive their outputs from arbitrary brain activity arising without the purpose of voluntary control, and are used instead for detecting the mental state (e.g., cognitive, affective) of the user. Passive BCIs have potential in many applications to enrich humanmachine interaction by providing implicit information on the user's state and adapting the environment accordingly. One application domain that has received particular attention is the monitoring of mental workload, particularly in safety-critical occupations like pilots, air-traffic controllers, and other industrial operators. The ultimate goal is to derive a continuous, objective estimation of the cognitive strain on the individual from physiological signal variables, so that appropriate action can be taken to reduce the potential for error during periods of extreme demand or overload. Mental workload detection also has potential value in other domains, including gaming, adaptive training, and user interface design applications, to enhance and personalize user experience.Previous studies support the feasibility of a passive BCI for automatic mental workload detection. However, to date only very basic functionality has been explored specifically, the automatic detection of brain activity associated with different levels of difficulty of a given task under controlled conditions. While this is a critical first step, there is still much work to be done before such systems are suitable for widespread practical application, and much opportunity remains for enhancing reliability and functionality. In this research program, we will build upon the current state-of-the-art in passive BCIs for automatic mental workload detection. Specifically, we will work toward developing a system that is both task- and emotion-aware. The enhanced capability will result in improved user-state prediction, which will in turn allow for enhanced human-machine interaction in a variety of applications.

脑机接口（BCI）技术传统上被定义为通过直接测量和翻译用户为了控制外部设备而有意产生的大脑活动来实现无运动通信的设备。这种“主动”BCI的目标用户通常是具有严重运动障碍的个体，其不能操作更常规的界面（例如，键盘）。虽然这仍然是大多数BCI研究的重点，但BCI对健康用户的潜力也得到了认可，并受到越来越多的关注。脑机接口的实际定义正在扩大，不仅包括那些使用大脑活动来取代残疾用户的自然中枢神经系统（CNS）输出的设备，还包括那些增强健康用户CNS输出的设备。这些所谓的“被动BCI”不是利用有意调制的大脑活动，而是从没有自愿控制目的的任意大脑活动中获得其输出，并用于检测精神状态（例如，认知的、情感的）。被动BCI在许多应用中具有通过提供关于用户状态的隐式信息并相应地适应环境来丰富人机交互的潜力。特别受到关注的一个应用领域是监测心理负荷，特别是在安全关键职业中，如飞行员，空中交通管制员和其他工业操作员。最终目标是从生理信号变量中获得对个体认知压力的连续客观估计，以便可以采取适当的行动来减少极端需求或过载期间的错误可能性。心理负荷检测在其他领域也有潜在的价值，包括游戏，自适应训练和用户界面设计应用程序，以增强和个性化的用户体验。然而，到目前为止，只有非常基本的功能已被具体探索，在受控条件下与给定任务的不同难度水平相关的大脑活动的自动检测。虽然这是关键的第一步，但在这种系统适合广泛的实际应用之前，仍有许多工作要做，并且仍有许多机会提高可靠性和功能性。在这项研究计划中，我们将建立在当前最先进的被动脑机接口自动精神负荷检测。具体来说，我们将致力于开发一个既能感知任务又能感知情感的系统。增强的功能将导致改进的用户状态预测，这反过来又允许在各种应用中增强人机交互。