Optimization of stimulus color for peripheral SSVEP-based brain-computer interfaces.
Overview
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
- CASIC-CQC Software Testing and Assessment Technology (Beijing) Co., Ltd., Beijing, China
- School of Optometry and Ophthalmology, Tianjin Medical University, Tianjin, China
Abstract
Background: Most existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience with system performance. Although recent studies have shown that peripheral vision stimulation can evoke SSVEPs with high user comfort, the impact of stimulus color on peripheral SSVEP performance remains underexplored. Therefore, this study attempted to investigate the effect of stimulus color on peripheral SSVEPs.
Methods: Four conventional stimulus colors (i.e., blue, green, red, and white) were evaluated using ultra-low frequency SSVEP stimuli, with the stimulation frequencies ranging from 2 Hz to 3.32 Hz. Based on the results, the optimized stimulus color was used to build a 12-target peripheral SSVEP-based BCI. Task-discriminant component analysis (TDCA) algorithm was adopted to detect SSVEPs. The feasibility of the proposed system was verified through offline experiments with 13 participants and online experiments with 11 participants.
Results: The offline experiments with 13 participants showed no significant differences in classification accuracy and information transfer rates (ITRs) among the four-color paradigms. However, green stimulation received the highest subjective comfort ratings. Consequently, green stimulation was selected for building the 12-target peripheral SSVEP-based BCI. The online results achieved a mean classification accuracy of 89.93 ± 6.10% and an ITR of 47.96 ± 6.98 bits/
Conclusion: The present findings support a comfort-driven color selection strategy for peripheral ultra-low-frequency SSVEP stimulation while maintaining comparable performance among the tested colors. These findings may provide practical guidance for more visually tolerable SSVEP-based BCI systems based on peripheral visual stimulation.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.
The paper's code and data availability statement is in the Data section.
Tracing map
A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.
Data
No dataset and no data link were found in the paper.
Data availability statement
The datasets presented in this article are not readily available because data and the programming code used as part of this research can be obtained from corresponding authors on reasonable request. Requests to access the datasets should be directed to .
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 5 funders, 49 references.
Cite
This paper
Liu, H., Li, Z., Li, W., Yang, R., & Chen, X. (2026). Optimization of stimulus color for peripheral SSVEP-based brain-computer interfaces. Frontiers in human neuroscience, 20, 1832475. https://
BibTeX
@article{liu2026optimiza
author = {Liu, Haochen and Li, Zhaohui and Li, Wenwen and Yang, Ruoqi and Chen, Xiaogang},
title = {{Optimization of stimulus color for peripheral SSVEP-based brain-computer interfaces}},
journal = {Frontiers in human neuroscience},
year = {2026},
month = jun,
volume = {20},
pages = {1832475},
publisher = {Frontiers Media SA},
issn = {1662-5161},
doi = {10.3389/
url = {https://
pmid = {42454243},
pmcid = {PMC13365271}
}
RIS
TY - JOUR
AU - Liu, Haochen
AU - Li, Zhaohui
AU - Li, Wenwen
AU - Yang, Ruoqi
AU - Chen, Xiaogang
TI - Optimization of stimulus color for peripheral SSVEP-based brain-computer interfaces
T2 - Frontiers in human neuroscience
J2 - Front Hum Neurosci
PY - 2026
DA - 2026/
VL - 20
SP - 1832475
SN - 1662-5161
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.3389/
"type": "article-journal",
"title": "Optimization of stimulus color for peripheral SSVEP-based brain-computer interfaces",
"container-title": "Frontiers in human neuroscience",
"author": [
{
"family": "Liu",
"given": "Haochen"
},
{
"family": "Li",
"given": "Zhaohui"
},
{
"family": "Li",
"given": "Wenwen"
},
{
"family": "Yang",
"given": "Ruoqi"
},
{
"family": "Chen",
"given": "Xiaogang"
}
],
"container-title-short":
"volume": "20",
"page": "1832475",
"DOI": "10.3389/
"PMID": "42454243",
"PMCID": "PMC13365271",
"ISSN": "1662-5161",
"publisher": "Frontiers Media SA",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
30
]
]
}
}
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.3389/fnhum.2026.1807535
- The ASME-speller: 30-class auditory brain-computer interface speller using stream segregation and the QWERTY layout.Journal: Frontiers in human neuroscienceIn common: EEG, 3 references
- [2] doi:10.3390/bs16081364
- Morphological Buffering in Neuro-Architecture: Interactive Effects of Color, Shape, and Area Proportion on Affective Processing.Journal: Behavioral sciences (Basel, Switzerland)In common: EEG, 2 references
- [3] doi:10.1038/s44385-026-00098-2 [code]
- Cross-region neural signal reconstruction to lift electrode placement constraints in SSVEP brain-computer interfaces.Journal: npj biomedical innovationsIn common: EEG, 1 reference
- [4] doi:10.1186/s12984-026-02065-9
- Decoding motor imagery related to major mimetic muscles from electroencephalography.Journal: Journal of neuroengineering and rehabilitationIn common: EEG, 1 reference
- [5] doi:10.1007/s10916-026-02374-5 [code]
- Attention-Enhanced U-Net for Sensor-Efficient High-Density EEG Reconstruction in Wearable Brain Monitoring Systems.Journal: Journal of medical systemsIn common: EEG, 1 reference
- [6] doi:10.1162/imag.a.1359 [code]
- Distinct roles of brain network flexibility in motor learning across age.Journal: Imaging neuroscience (Cambridge, Mass.)In common: EEG, 1 reference
- [7] doi:10.1038/s41467-026-75653-x [code]
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Journal: Nature communicationsIn common: EEG, 1 reference
- [8] doi:10.1073/pnas.2538139123
- Cognition does not automatically influence perception: Evidence from neural encoding of colors belonging to different categories.Journal: Proceedings of the National Academy of Sciences of the United States of AmericaIn common: EEG, 1 reference
- [9] doi:10.1186/s40708-026-00308-y
- Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.Journal: Brain informaticsIn common: EEG, 1 reference
- [10] doi:10.3390/brainsci16080873
- Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.Journal: Brain sciencesIn common: EEG, 1 reference
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.