Reevaluating performance in c-VEP BCIs: The impact of calibration time

• Calibration duration is critical for balancing accuracy, speed, and visual comfort in c-VEP-based BCIs. • Two datasets involving non-binary codes and spatial frequency variations in checkerboard-like stimuli have been made publicly available. • Achieving 95 % decoding accuracy within 3 s requires an average calibration of 15 s for binary stimuli and 98 s for non-binary stimuli. • A minimum 1-minute calibration is crucial to achieve a stable estimation of the c-VEP response using the reference processing pipeline. Code-modulated visual evoked potentials (c-VEP) have demonstrated high performance in non-invasive brain-computer interfaces (BCIs). Recently, research has begun to consider practical aspects such as visual comfort, where non-binary sequences and variations in the spatial frequency of stimuli play significant roles. However, calibration requirements remain underexplored in performance comparisons. This study aims to analyze a multi-variable tradeoff crucial to the practical application of c-VEP-based BCIs: decoding accuracy, decoding speed, and calibration time. Visual comfort is retrospectively evaluated using two pre-recorded datasets. Models were trained with increasing calibration cycles and tested across varying decoding times, depicting learning and decoding curves. The datasets comprised 32 healthy subjects, and featured different stimulus paradigms: plain non-binary stimuli and checkerboard-like binary stimuli with spatial frequency variations. Results showed that all conditions achieved over 97 % grand-averaged accuracy with sufficient calibration. However, a clear tradeoff emerged between calibration duration and performance. Achieving 95 % average accuracy within a 2 s decoding window required mean calibration durations of 28.7 ± 19.0 s for binary stimuli, or 148.7 ± 72.3 s for non-binary stimuli. The binary checkerboard-based condition with a spatial frequency of 1.2 c/º (C016) proved to be particularly effective, achieving over 95 % accuracy within 2 s decoding window using only 7.3 s of calibration, and reporting a significant improvement in visual comfort. A minimum calibration time of 1 min was considered essential to adequately estimate the brain response, critical in template-matching paradigms. In conclusion, achieving optimal c-VEP performance requires balancing calibration duration, decoding speed and accuracy, and visual comfort.

Authors

Institutions

Publication Details

Journal
Journal of Applied Biomedicine
Published
2025-10-01
DOI
https://doi.org/10.1016/j.bbe.2025.10.006
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reevaluating performance in c-VEP BCIs: The impact of calibration time

Víctor Martínez-Cagigal, Roberto Hornero, Sergio Pérez-Velasco, Eduardo Santamaría-Vázquez et al.
Journal of Applied Biomedicine
EEG and Brain-Computer Interfaces
article

Reevaluating performance in c-VEP BCIs: The impact of calibration time

Víctor Martínez-Cagigal, Roberto Hornero, Sergio Pérez-Velasco, Eduardo Santamaría-Vázquez, Ana Martín-Fernández
article en

Abstract

• Calibration duration is critical for balancing accuracy, speed, and visual comfort in c-VEP-based BCIs. • Two datasets involving non-binary codes and spatial frequency variations in checkerboard-like stimuli have been made publicly available. • Achieving 95 % decoding accuracy within 3 s requires an average calibration of 15 s for binary stimuli and 98 s for non-binary stimuli. • A minimum 1-minute calibration is crucial to achieve a stable estimation of the c-VEP response using the reference processing pipeline. Code-modulated visual evoked potentials (c-VEP) have demonstrated high performance in non-invasive brain-computer interfaces (BCIs). Recently, research has begun to consider practical aspects such as visual comfort, where non-binary sequences and variations in the spatial frequency of stimuli play significant roles. However, calibration requirements remain underexplored in performance comparisons. This study aims to analyze a multi-variable tradeoff crucial to the practical application of c-VEP-based BCIs: decoding accuracy, decoding speed, and calibration time. Visual comfort is retrospectively evaluated using two pre-recorded datasets. Models were trained with increasing calibration cycles and tested across varying decoding times, depicting learning and decoding curves. The datasets comprised 32 healthy subjects, and featured different stimulus paradigms: plain non-binary stimuli and checkerboard-like binary stimuli with spatial frequency variations. Results showed that all conditions achieved over 97 % grand-averaged accuracy with sufficient calibration. However, a clear tradeoff emerged between calibration duration and performance. Achieving 95 % average accuracy within a 2 s decoding window required mean calibration durations of 28.7 ± 19.0 s for binary stimuli, or 148.7 ± 72.3 s for non-binary stimuli. The binary checkerboard-based condition with a spatial frequency of 1.2 c/º (C016) proved to be particularly effective, achieving over 95 % accuracy within 2 s decoding window using only 7.3 s of calibration, and reporting a significant improvement in visual comfort. A minimum calibration time of 1 min was considered essential to adequately estimate the brain response, critical in template-matching paradigms. In conclusion, achieving optimal c-VEP performance requires balancing calibration duration, decoding speed and accuracy, and visual comfort.

Journal of Applied BiomedicineVol. 45(4)
Universidad de Valladolid (ES), Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES), Instituto de Biomedicina y Genética Molecular de Valladolid (ES)
Openalex Percentile: Top 65%
EEG and Brain-Computer Interfaces
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.