Putze Felix, Herff Christian, Tremmel Christoph, Schultz Tanja, Krusienski Dean J
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:3103-3106. doi: 10.1109/EMBC.2019.8856386.
Virtual Reality (VR) has emerged as a novel paradigm for immersive applications in training, entertainment, rehabilitation, and other domains. In this paper, we investigate the automatic classification of mental workload from brain activity measured through functional near-infrared spectroscopy (fNIRS) in VR. We present results from a study which implements the established n-back task in an immersive visual scene, including physical interaction. Our results show that user workload can be detected from fNIRS signals in immersive VR tasks both person-dependently and -adaptively.
虚拟现实（VR）已成为一种新颖的范式，用于训练、娱乐、康复及其他领域的沉浸式应用。在本文中，我们研究了通过功能近红外光谱（fNIRS）测量的大脑活动对VR中精神负荷的自动分类。我们展示了一项研究的结果，该研究在沉浸式视觉场景中实施了既定的n-back任务，包括身体互动。我们的结果表明，在沉浸式VR任务中，可以从fNIRS信号中依赖个体并自适应地检测用户负荷。
