Swami Piyush, Gramann Klaus, Vonstad Elise Klæbo, Vereijken Beatrix, Holt Alexander, Holt Tomas, Sandstrak Grethe, Nilsen Jan Harald, Su Xiaomeng
Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Front Hum Neurosci. 2023 Sep 19;17:1223774. doi: 10.3389/fnhum.2023.1223774. eCollection 2023.
To investigate event-related activity in human brain dynamics as measured with EEG, triggers must be incorporated to indicate the onset of events in the experimental protocol. Such triggers allow for the extraction of ERP, i.e., systematic electrophysiological responses to internal or external stimuli that must be extracted from the ongoing oscillatory activity by averaging several trials containing similar events. Due to the technical setup with separate hardware sending and recording triggers, the recorded data commonly involves latency differences between the transmitted and received triggers. The computation of these latencies is critical for shifting the epochs with respect to the triggers sent. Otherwise, timing differences can lead to a misinterpretation of the resulting ERPs. This study presents a methodical approach for the CLET using a photodiode on a non-immersive VR (i.e., LED screen) and an immersive VR (i.e., HMD). Two sets of algorithms are proposed to analyze the photodiode data. The experiment designed for this study involved the synchronization of EEG, EMG, PPG, photodiode sensors, and ten 3D MoCap cameras with a VR presentation platform (Unity). The average latency computed for LED screen data for a set of white and black stimuli was 121.98 ± 8.71 ms and 121.66 ± 8.80 ms, respectively. In contrast, the average latency computed for HMD data for the white and black stimuli sets was 82.80 ± 7.63 ms and 69.82 ± 5.52 ms. The codes for CLET and analysis, along with datasets, tables, and a tutorial video for using the codes, have been made publicly available.
为了研究通过脑电图（EEG）测量的人类脑动力学中的事件相关活动，必须在实验方案中加入触发信号以指示事件的开始。这些触发信号有助于提取事件相关电位（ERP），即对内部或外部刺激的系统性电生理反应，必须通过对包含相似事件的多次试验进行平均，从正在进行的振荡活动中提取出来。由于采用了单独的硬件发送和记录触发信号的技术设置，记录的数据通常涉及发送和接收触发信号之间的延迟差异。这些延迟的计算对于相对于发送的触发信号移动时间段至关重要。否则，时间差异可能导致对所得ERP的错误解释。本研究提出了一种使用非沉浸式虚拟现实（即LED屏幕）和沉浸式虚拟现实（即头戴式显示器，HMD）上的光电二极管进行CLET的系统方法。提出了两组算法来分析光电二极管数据。本研究设计的实验涉及将脑电图、肌电图、光电容积脉搏波描记法（PPG）、光电二极管传感器和十个3D运动捕捉摄像头与一个虚拟现实展示平台（Unity）进行同步。一组白色和黑色刺激的LED屏幕数据计算出的平均延迟分别为121.98±8.71毫秒和121.66±8.80毫秒。相比之下，白色和黑色刺激集的HMD数据计算出的平均延迟分别为82.80±7.63毫秒和69.82±5.52毫秒。CLET及其分析的代码，连同数据集、表格以及使用这些代码的教程视频，均已公开提供。
