Methods

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.

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

Grouped by dataset. Compare figures within a group only: across groups the task, cohort, chance level and electrode layout all change.

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

Read with its protocol: 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: chance level where the payload records one.

Method	Condition	Metric	Value	People
FBCCA	Wet recording · no source training	Balanced accuracy	82.3%78.7%–85.7%	102
FBCCA	Dry recording · no source training	Balanced accuracy	64.6%59.5%–69.6%	102

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