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

脑机接口(BCI)技术传统上被定义为通过直接测量和转换由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人，他们不能操作更传统的接口(例如，键盘)。尽管这仍然是大多数脑机接口研究的重点，但脑机接口对健康用户的潜力也已被认识到，并受到越来越多的关注。BCI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的CNS输出的设备。这些所谓的“被动BCI”不是利用有意调节的大脑活动，而是从没有自愿控制目的的任意大脑活动中获得它们的输出，并被用来检测用户的精神状态(例如，认知、情感)。被动式BCI通过提供关于用户状态的隐含信息并相应地适应环境，在许多应用中具有丰富人机交互的潜力。一个受到特别关注的应用领域是脑力劳动的监控，特别是在飞行员、空中交通管制员和其他工业操作员等安全关键型职业中。最终目标是从生理信号变量中得出对个体认知压力的连续、客观的估计，以便采取适当的行动，以减少在极端需求或超载期间出错的可能性。脑力工作负荷检测在其他领域也有潜在价值，包括游戏、自适应培训和用户界面设计应用程序，以增强和个性化用户体验。

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 human–machine 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.