A Feasible BCI in Real Life: Using Predicted Head Rotation to Improve HMD Imaging

A. Brouwer, J. V. D. Waa, M. Hogervorst, A. Cacace, H. Stokking
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引用次数: 5

Abstract

While brain signals potentially provide us with valuable information about a user, it is not straightforward to derive and use this information to smooth man-machine interaction in a real life setting. We here propose to predict head rotation on the basis of brain signals in order to improve images presented in a Head Mounted Display (HMD). Previous studies based on arm and leg movements suggest that this could be possible, and a pilot study showed promising results. From the perspective of the field of Brain-Computer Interfaces (BCI), this application provides a good case to put the field's achievements to the test and to further develop in the context of a real life application. The main reason for this is that within the proposed application, acquiring accurately labeled training data (whether and which head movement took place) and monitoring of the quality of the predictive model can happen on the fly. From the perspective of HMD technology and Intelligent User Interfaces, the proposed BCI potentially improves user experience and enables new types of immersive applications.
现实生活中可行的脑机接口:使用预测的头部旋转来改善HMD成像
虽然大脑信号可能为我们提供有关用户的有价值的信息,但在现实生活中提取和使用这些信息来平滑人机交互并不简单。我们在此提出基于大脑信号预测头部旋转,以改善头戴式显示器(HMD)中呈现的图像。先前基于手臂和腿部运动的研究表明,这是可能的,一项初步研究显示了有希望的结果。从脑机接口(BCI)领域的角度来看,该应用程序提供了一个很好的案例,将该领域的成果进行测试,并在现实生活应用的背景下进一步发展。这样做的主要原因是,在提议的应用程序中,获取准确标记的训练数据(是否以及发生了哪些头部运动)和监测预测模型的质量可以在飞行中发生。从HMD技术和智能用户界面的角度来看,提议的BCI有可能改善用户体验,并实现新型沉浸式应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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