Critical task and cognition-based environments, such as in military and defense operations, aviation user-technology interaction evaluation on UI, understanding intuitiveness of a hardware model or software toolkit, etc. require an assessment of how much a particular task is generating mental workload on a user. This is necessary for understanding how those tasks, operations, and activities can be improvised and made better suited for the users so that they reduce the mental workload on the individual and the operators can use them with ease and less difficulty. However, a particular task can be gauged by a user as simple while for others it may be difficult. Understanding the complexity of a particular task can only be done on user level and we propose to do this by understanding the mental workload (MWL) generated on an operator while performing a task which requires processing a lot of information to get the task done. In this work, we have proposed an experimental setup which replicates modern day workload on doing regular day job tasks. We propose an approach to automatically evaluate the task complexity perceived by an individual by using electroencephalogram (EEG) data of a user during operation. Few crucial steps that are addressed in this work include extraction and optimization of different features and selection of relevant features for dimensionality reduction and using supervised machine learning techniques. In addition to this, performance results of the classifiers are compared using all features and also using only the selected features. From the results, it can be inferred that machine learning algorithms perform better as compared to traditional approaches for mental workload estimation.
翻译:关键任务和基于认知的环境,例如军事和国防行动、航空用户-技术对UI的互动评价、理解硬件模型或软件工具包的直观性、理解硬件模型或软件工具包的直观性等等,都需要评估一项特定任务给用户带来的精神工作量。这对于了解如何使这些任务、行动和活动能够简易,使用户更适合用户,从而减少个人和操作者在正常日常工作任务中可以轻松和较少困难地使用这些任务和任务,从而减少个人和操作者的精神工作量。然而,一个特定任务可以由用户简单地衡量,而对于其他人来说则可能比较困难。了解特定任务的复杂性只能在用户一级做,我们提议通过了解操作者产生的精神工作量(MWL),来完成这项工作。 评估某一特定任务的复杂性,我们建议通过理解操作者产生的精神工作量(MWL),同时开展一项需要处理大量信息来完成的任务。 在这项工作中,我们提议建立一个实验性设置,在日常日常工作任务中复制现代的工作量。 我们提议一种办法,通过使用用户的电脑图(EEEG)数据来自动评估个人对任务的复杂性进行评估。 了解具体任务的复杂性,只有在用户在操作中采用用户的电算方法,在进行更精确的精测时,在进行较细的精细的学习时,在进行对比中采用各种的精细的精细的精细的精细的精细的精细的精细的精细的精细的精细的精细的精细的精细工作上,在进行。