# FBCCA: results on public EEG datasets

Methods

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. ↗](https://doi.org/10.1088/1741-2560/12/4/046008)

Description sources [pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/26035476/) · [doi.org](https://doi.org/10.1088/1741-2560/12/4/046008) · [iopscience.iop.org](https://iopscience.iop.org/article/10.1088/1741-2560/12/4/046008) · [pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC5783827/) · [github.com](https://github.com/mnakanishi/TRCA-SSVEP/blob/master/src/test_fbcca.m)

## 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)](https://bci.report/datasets/wearable-ssvep-102/) · Dry vs. wet sensor transfer

Read with its protocol: [Dry vs. wet electrodes](https://bci.report/topics/dry-vs-wet/) · [deployment-topics.json](https://bci.report/data/deployment-topics.json)

**Balanced accuracy, 2 configurations.** Dot: the estimate; line: 95% interval; dashed line: chance level where the payload records one.

- FBCCA · Wet recording · no source training: 82.3% (78.7%–85.7%)
- FBCCA · Dry recording · no source training: 64.6% (59.5%–69.6%)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [FBCCA](https://bci.report/methods/fbcca/) | Wet recording · no source training | Balanced accuracy | 82.3% (78.7%–85.7%) | 102 |
| [FBCCA](https://bci.report/methods/fbcca/) | Dry recording · no source training | Balanced accuracy | 64.6% (59.5%–69.6%) | 102 |

[All methods →](https://bci.report/methods/) · [All datasets →](https://bci.report/datasets/)

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Markdown copy of https://bci.report/methods/fbcca/, generated from the published page. Figures are aggregate results; terms of use: https://bci.report/data-use/