CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses

Piyush Swami; Klaus Gramann; Elise Klæbo Vonstad; Beatrix Vereijken; Alexander Holt; Tomas Holt; Grethe Sandstrak; Jan Harald Nilsen; Xiaomeng Su

doi:10.3389/fnhum.2023.1223774

CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses

Front Hum Neurosci. 2023 Sep 19:17:1223774. doi: 10.3389/fnhum.2023.1223774. eCollection 2023.

Authors

Affiliations

¹ Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
² Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
³ Biomedical Engineering Techies, Broendby, Denmark.
⁴ Biological Psychology and Neuroergonomics, Technical University of Berlin, Berlin, Germany.
⁵ Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.

Abstract

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.

Keywords: electroencephalography (EEG); event-related potential (ERP); interface; latencies; motion-capture (Mocap).

Grants and funding

This research was funded by the European Research Consortium for Informatics and Mathematics, and the Norwegian University of Science and Technology, Trondheim, Norway, from 2019–21.