ISSN Print 2500–1094
ISSN Online 2542–1204
BIOMEDICAL JOURNAL OF PIROGOV UNIVERSITY (MOSCOW, RUSSIA)
Copyright: © 2023 by the authors. Licensee: Pirogov University.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (CC BY).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (CC BY).
ORIGINAL RESEARCH
Sources and impact of human brain potential variability in the brain-computer interface
About authors
1 Lomonosov Moscow State University, Moscow, Russia
2 Neurocognitive Research Center (MEG Center), Moscow State University of Psychology and Education, Moscow, Russia
Correspondence should be addressed: Ilya P. Ganin
Leninskiye Gory, 1, str. 12, k. 246, Moscow, 119234, Russia; ur.liam@ninagpi
About paper
Funding: the study was supported by the Russian Science Foundation Grant № 21-75-00021, https://rscf.ru/project/21-75-00021/
Acknowledgements: the authors would like to thank Yu. Nuzhdin (Kurchatov Institute) for for developing and supporting software for EEG recording used to perform the study
Author contribution: Ganin IP — conducting research, data analysis and interpretation, literature review, manuscript writing; Vasilyev AN — data analysis and interpretation, literature review, manuscript writing; Glazova TD — conducting research, literature review; Kaplan AYa — data interpretation.
Compliance with ethical standards: the study was approved by the Ethics Committee of the Lomonosov Moscow State University (protocol № 113-d of 19 June 2020); the informed consent was submitted by all study participants.
Received: 2023-04-14
Accepted: 2023-04-27
Published online: 2023-04-28
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Fig. 1. The stimulus matrix P300 BCI used in the study. The matrix was located in the center of the screen on the black background. “Ordinary matrix” is on the left, “half-empty matrix” is on the right
Fig. 2. The extracted spatial components N1 and P300. The figure above shows topography of the spatial filter patterns. The figure below shows components N1 and P300 averaged across all subjects. Vertical axis — normalized amplitude in arbitrary units; horizontal axis — time (s). The vertical dotted line (0 s) corresponds to the stimulus onset. N = 19 subjects
Fig. 3. The average classification accuracy with different number of the stimulus sequences calculated for various signal feature sets used by the classifier — usual 11 EEG electrodes, extracted spatial components N1 and P300 (no latency correction, latency correction applied to N1 only, to P300 only, or to both components, N1 and P300). The mean and standard error of the mean are provided. N = 19 subjects
Table 1. The average amplitudes of the N1 and P300 components in all modes when using the standard averaging method (no latency correction) and when averaging the epochs adjusted to latency of the appropriate component. The mean and standard error of the mean are provided. N = 19 subjects
Table 2. The average absolute latencies and the average indicators of their variability (MAD) for the N1 and P300 components in all modes. The mean and standard error of the mean are provided. N = 19 subjects
Table 3. The average classification accuracy obtained in all modes for one or two stimulus sequences that has been calculated for various signal feature sets used by the classifier — usual 11 EEG electrodes and the extracted spatial components N1 and P300 with or without peak latency correction. The mean and standard error of the mean are provided. N = 19 subjects
