Objective: In recent years, the training of novice medical professionals with simulated environments such as virtual reality (VR) and augmented reality (AR) has increased dramatically. However, the usability of these technologies is limited due to the complexity involved in creating the clinical content. To be comparable to a clinical environment, the simulation platform should include real-world learning parameters such as patient physiology, emotions, and clinical team behaviors. Incorporating such nondeterministic parameters has historically required faculty to possess advanced programming skills. Lack of effective software for instructors to easily develop VR curriculum content is a hurdle in developing VR based curriculum. Method: We address this challenge through a software platform that simplifies the creation of Interactive Mixed Reality (IMR) scenarios. Three educational components we were able to embed into an IMR scenario includes 1) integrated 360-degree video recording of a clinical encounter to provide a first-person perspective, 2) rich annotated knowledge content, and 3) assessment questionnaire. We developed a sepsis prevention education scenario using the IMR software to demonstrate the potential of enhancing simulated medical training by accelerating clinical exposure for novice students. Result: An IRB approved study was conducted with a group of 28 novice students to evaluate the efficacy of the IMR technology. The participants provided feedback by answering demographics, NASA-TLX and system usability scale questionnaires. Significance: Our software is a step towards improving VR based education content development process. Conclusion: The studies conducted here provide preliminary evidence that the IMR software is a usable technology based on the NASA-TLX and system usability studies conducted. Future work will compare our new educational strategy for medical training with live simulation scenarios inside a hospital room and a simple video-based curriculum.

Summary form only given. In the video, experiments demonstrating the control of robot using a brain computer interface are shown. This is done using “actemes”. Actemes are fundamental units of action. The actemes can be combined to perform complex tasks. The user who performs the tasks imagines the complex actions as an ordered combination of actemes. The EEG of the user during the process is collected and classified in real-time using independent component analysis (ICA)[1, 2] and a neural network classifier[3, 4] to identify the actemes. This ordered set of actemes can be considered to be meaningful sentence in a language. The order in which the complex action is constructed is called the grammar. Two such tasks are demonstrated using these experiments. The first task involves insertion of round peg into a hole and rotating the peg inorder to simulate the insertion of a screw into threaded hole. The actemes involved are “insert” and “rotate”. The second task is a point-to-point movement of a peg in which the peg is to be moved from a circular zone at the bottom right to the circular zone centered at the cross hair at the top left of the screen. The representative image is shown in Fig 1.