The Effect of Monitor Raster Latency on VEPs, ERPs and Brain-Computer Interface Performance
Overview
Affiliations
Background: Visual neuroscience experiments and Brain-Computer Interface (BCI) control often require strict timings in a millisecond scale. As most experiments are performed using a personal computer (PC), the latencies that are introduced by the setup should be taken into account and be corrected. As a standard computer monitor uses a rastering to update each line of the image sequentially, this causes a monitor raster latency which depends on the position, on the monitor and the refresh rate.
New Method: We technically measured the raster latencies of different monitors and present the effects on visual evoked potentials (VEPs) and event-related potentials (ERPs). Additionally we present a method for correcting the monitor raster latency and analyzed the performance difference of a code-modulated VEP BCI speller by correcting the latency.
Comparison With Existing Methods: There are currently no other methods validating the effects of monitor raster latency on VEPs and ERPs.
Results: The timings of VEPs and ERPs are directly affected by the raster latency. Furthermore, correcting the raster latency resulted in a significant reduction of the target prediction error from 7.98% to 4.61% and also in a more reliable classification of targets by significantly increasing the distance between the most probable and the second most probable target by 18.23%.
Conclusions: The monitor raster latency affects the timings of VEPs and ERPs, and correcting resulted in a significant error reduction of 42.23%. It is recommend to correct the raster latency for an increased BCI performance and methodical correctness.
Low-cost, mobile EEG hardware for SSVEP applications.
Kancaoglu M, Kuntalp M HardwareX. 2024; 19:e00567.
PMID: 39687306 PMC: 11647867. DOI: 10.1016/j.ohx.2024.e00567.
Amin H, Ullah R, Reza M, Malik A J Neuroeng Rehabil. 2023; 20(1):70.
PMID: 37269019 PMC: 10236727. DOI: 10.1186/s12984-023-01179-8.
Han C, Xu G, Zheng X, Tian P, Zhang K, Yan W Front Neurosci. 2022; 15:757679.
PMID: 35069096 PMC: 8777290. DOI: 10.3389/fnins.2021.757679.
World's fastest brain-computer interface: Combining EEG2Code with deep learning.
Nagel S, Spuler M PLoS One. 2019; 14(9):e0221909.
PMID: 31490999 PMC: 6730910. DOI: 10.1371/journal.pone.0221909.
Gembler F, Stawicki P, Saboor A, Volosyak I PLoS One. 2019; 14(6):e0218177.
PMID: 31194817 PMC: 6564540. DOI: 10.1371/journal.pone.0218177.