BCI ReportClassical method
CCA: results on public EEG datasets
CCA-based SSVEP detection, proposed by Lin, Zhang, Wu and Gao (Tsinghua University; IEEE Transactions on Biomedical Engineering, 2006), computes the canonical correlation between multichannel EEG and sine–cosine reference signals at each candidate stimulus frequency and its harmonics. The frequency with the largest correlation is selected; the references are generated from the stimulus frequencies, not learned from recorded trials.
Measured on: BETA; Wearable SSVEP BCI dataset (dry and wet electrodes); Mobile BCI dataset (SSVEP and ERP paradigms) · Protocols: SSVEP · 8 channels; SSVEP · 4 channels
Also known as CCA · Standard CCA · canonical correlation analysis · Lin et al. 2006
Directory status: Evaluated · Implementation used here ↗ · Description sources pubmed.ncbi.nlm.nih.gov · pubmed.ncbi.nlm.nih.gov · doi.org · doi.org · pmc.ncbi.nlm.nih.gov · github.com · iopscience.iop.org
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
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Standard CCA | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 63.1%57.2%–69.0% | 70 |
BETA · Core matrix · SSVEP, 4 channels
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Standard CCA | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 57.6%51.8%–63.5% | 70 |
BETA · Reliable decisions · when to decline a decision
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Standard CCA | Fixed threshold: calibrated confidence at least 0.8 | Trials accepted | 3,176 / 11,2000 of 70 people had nothing accepted. | 70 |
| Standard CCA | Learned reject option minus calibrated confidence, same classifier | Error among the 80% most certain, difference | +0.3 pp−0.3 pp–+1.0 ppNo difference resolved: the interval includes zero. | 70 |
Limits: Uniform target prior by design; cue-paced lab spelling with no idle state, so no false-activation figure of any kind. Calibration data are participant-disjoint inner out-of-fold scores (cross-fit). Inner models see about 3/4 of the training people, so they may be less confident than the full model whose scores are tested. This approximation is part of every calibrated figure. Rejecting likely errors on known classes is not detecting unfamiliar input. An uncertainty threshold may not flag an unseen class (https://arxiv.org/abs/2603.13324). Route 1 makes no out-of-distribution claim. A contrast is called a difference only when its paired 95% participant-bootstrap interval excludes 0 (no multiplicity correction); otherwise "no difference resolved". No ranking where intervals overlap. Descriptive entries are never called differences. Every limitation of route 1 →
Wearable SSVEP BCI dataset (dry and wet electrodes) · Dry vs. wet sensor transfer
Wearable SSVEP BCI dataset (dry and wet electrodes) · Calibration budget · eTRCA and CCA
Mobile BCI dataset (SSVEP and ERP paradigms) · Movement · SSVEP, 5-second windows
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Author-style CCA | Standing · scalp · 8 ch | Balanced accuracy | 88.7%80.0%–95.9% | 23 |
| Author-style CCA | Slow walk · 0.8 m/s · scalp · 8 ch | Balanced accuracy | 82.9%74.3%–90.4% | 23 |
| Author-style CCA | Fast walk · 1.6 m/s · scalp · 8 ch | Balanced accuracy | 80.9%72.0%–88.8% | 23 |
| Author-style CCA | Standing · ear · 14 ch | Balanced accuracy | 53.1%47.4%–59.6% | 23 |
| Author-style CCA | Slow walk · 0.8 m/s · ear · 14 ch | Balanced accuracy | 43.4%39.6%–47.8% | 23 |
| Author-style CCA | Fast walk · 1.6 m/s · ear · 14 ch | Balanced accuracy | 39.1%36.7%–41.8% | 23 |
Mobile BCI dataset (SSVEP and ERP paradigms) · Movement · SSVEP, 2-second windows
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Author-style CCA | Standing · scalp · 8 ch | Balanced accuracy | 82.3%74.6%–89.4% | 23 |
| Author-style CCA | Slow walk · 0.8 m/s · scalp · 8 ch | Balanced accuracy | 70.7%62.3%–78.6% | 23 |
| Author-style CCA | Fast walk · 1.6 m/s · scalp · 8 ch | Balanced accuracy | 68.6%60.1%–76.7% | 23 |
Cite this page
BCI Report (2026). CCA: results on public EEG datasets. https://bci.report/methods/cca/
Figures from releases reliable-decisions-update-20261004 (2026-10-04), 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 ↗