Berka Chris, Levendowski Daniel J, Lumicao Michelle N, Yau Alan, Davis Gene, Zivkovic Vladimir T, Olmstead Richard E, Tremoulet Patrice D, Craven Patrick L
Advanced Brain Monitoring, Inc., 2850 Pio Pico Drive, Suite A, Carlsbad, CA 92008, USA.
Aviat Space Environ Med. 2007 May;78(5 Suppl):B231-44.
The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity.
The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG.
Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics.
These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.
在操作环境中持续且不引人注意地监测任务参与度和心理负荷水平的能力，对于确定人类与技术交互的更准确、高效方法可能很有用。这些信息还可用于优化更安全、高效的工作环境设计，提高积极性和生产力。
本研究探讨了在认知测试过程中，以不引人注意的方式获取并量化脑电图（EEG）参与度和负荷指标的可行性。在包括多级向前/向后数字广度、网格回忆、连线测验、心算、20分钟三选一警觉性、图像学习和记忆测试等任务期间，使用无线传感器头戴式设备（F3-F4、C3-C4、Cz-POz、F3-Cz、Fz-C3、Fz-POz）从80名健康参与者采集EEG。每1秒的EEG计算参与度和负荷的EEG指标。
在20分钟的警觉性测试中，参与者的参与度下降，但负荷没有下降。与识别/回忆期相比，在言语和图像学习及记忆测试的编码期，参与度和负荷显著增加。在向前和向后数字广度、网格回忆和心算测试中，随着难度水平增加，负荷呈线性增加，但参与度没有增加。EEG测量与主观和客观绩效指标相关。
这些数据与先前的研究表明，EEG参与度反映信息收集、视觉处理和注意力分配。EEG负荷随着工作记忆负荷增加以及在解决问题、信息整合、分析推理过程中增加，可能更能反映执行功能。在逐秒时间尺度上检查EEG发现，当与特定任务事件对齐时，负荷与参与度水平之间存在关联，这提供了初步证据，表明逐秒分类反映了任务绩效参数。
