FBCCA: results on public EEG datasets

Classical method

FBCCA: results on public EEG datasets

Filter bank canonical correlation analysis (FBCCA), proposed by Chen, Wang, Gao, Jung and Gao (Journal of Neural Engineering, 2015), extends CCA-based SSVEP detection: a filter bank splits the EEG into several sub-bands, CCA against sine–cosine references is computed in each, and a weighted combination of the sub-band correlations selects the target frequency. It was introduced with a 40-target SSVEP speller.

Measured on: BETA; Wearable SSVEP BCI dataset (dry and wet electrodes) · Protocols: SSVEP · 8 channels; SSVEP · 4 channels

Also known as FBCCA · filter bank CCA · filter bank canonical correlation analysis · Chen et al. 2015

Reference Chen X, Wang Y, Gao S, Jung T-P, Gao X. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface. Journal of Neural Engineering 12(4):046008, 2015. ↗

Description sources pubmed.ncbi.nlm.nih.gov · doi.org · iopscience.iop.org · pmc.ncbi.nlm.nih.gov · github.com

Published results

Compare figures within a group only: task, cohort, chance level and electrode layout can differ between groups. One row is above another only where their 95% intervals do not overlap.

BETA · Core matrix · SSVEP, 8 channels

Source page: Core-matrix protocol: SSVEP · 8 channels · Chance level 2.5% · core-matrix-20261106-beta-8ch.csv

Method	Condition	Metric	Value	People
Filter-bank CCA (FBCCA-M3-N7-H5)	40 visual targets · 8 posterior electrodes	Balanced accuracy	79.1%75.1%–82.8%FBCCA-M3-N7-H5 · seven Chebyshev sub-bands, five harmonics · training-free · 250 Hz	70

BETA · Core matrix · SSVEP, 4 channels

Source page: Core-matrix protocol: SSVEP · 4 channels · Chance level 2.5% · core-matrix-20261106-beta-4ch.csv

Method	Condition	Metric	Value	People
Filter-bank CCA (FBCCA-M3-N7-H5)	40 visual targets · 4 posterior electrodes	Balanced accuracy	72.3%67.5%–76.9%FBCCA-M3-N7-H5 · seven Chebyshev sub-bands, five harmonics · training-free · 250 Hz	70

Wearable SSVEP BCI dataset (dry and wet electrodes) · Dry vs. wet sensor transfer

Source page: Dry vs. wet electrodes · deployment-topics.json

FBCCA · Wet recording · no source training

82.3% (78.7%–85.7%)

FBCCA · Dry recording · no source training

64.6% (59.5%–69.6%)

Balanced accuracy, 2 configurations. Dot: the estimate; line: 95% interval; dashed line, where drawn: chance level.

Method	Condition	Metric	Value	People
FBCCA	Wet recording · no source training	Balanced accuracy	82.3%78.7%–85.7%Five Butterworth order-4 sub-bands (6–80 to 38–80 Hz), four harmonics	102
FBCCA	Dry recording · no source training	Balanced accuracy	64.6%59.5%–69.6%Five Butterworth order-4 sub-bands (6–80 to 38–80 Hz), four harmonics	102

All methods → · All datasets →

Cite this page

BCI Report (2026). FBCCA: results on public EEG datasets. https://bci.report/methods/fbcca/

Figures from releases core-matrix-update-20261106 (2026-11-06), deployment-topics-20260920-v1 (2026-09-20), research-preview-20260920 (2026-09-20). Cite the upstream datasets as well: each dataset’s page gives its credit.

Every release is archived on Zenodo: doi:10.5281/zenodo.23123296. BibTeX for the site and its releases → · CITATION.cff ↗