# SSVEP-016: Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs

> 本页由本地论文卡片生成。请不要直接编辑本页；修改源卡片后运行 `vp run docs:generate`。

> 中文支持说明：本页是中文站点镜像，保留论文标题、证据状态和来源字段的原始表述，避免未经核实的翻译改变证据边界。

> 内部 wiki 边界：本页提供论文卡片证据和用户提供的 PDF 下载入口，仅用于私有阅读；全文抽取文本不发布。

> 来源: `library/paper_cards/SSVEP-016.md`

## 论文访问

* 内部 PDF: <a href={"/papers/SSVEP-016.pdf"} download style={{ display: "inline-flex", alignItems: "center", justifyContent: "center", minHeight: "2.25rem", padding: "0.45rem 0.8rem", borderRadius: "6px", backgroundColor: "#047857", color: "#ffffff", fontWeight: 700, lineHeight: 1, textDecoration: "none", boxShadow: "0 1px 2px rgba(15, 23, 42, 0.22)" }}>下载论文 PDF</a>
* DOI / 官方页面: [10.1109/TBME.2006.886577](https://doi.org/10.1109/TBME.2006.886577)
* 部署边界: 这些 PDF 链接只适合私有/受保护的内部 wiki。

## SSVEP-016: Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs

## Metadata

* ID: SSVEP-016
* Title: Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
* Year: 2007
* DOI / URL: 10.1109/TBME.2006.886577
* Local PDF: 见上方论文访问区块
* Text artifact: local-only path withheld from docs site
* Review status: `extracted`

## Study Type

* Track: SSVEP
* Task: SSVEP frequency recognition using canonical correlation analysis
* Participants or dataset: local text reports subject-specific runs; exact subject count needs confirmation
* Device/electrode setup: multichannel EEG; local text discusses 8 channels and channel selection
* Protocol/task: frequency-coded SSVEP recognition

## Methods

* Signal processing or analysis: CCA between multichannel EEG and sinusoidal reference signals
* Comparator: FFT/power-spectrum based recognition
* Training/calibration: channel selection and cross-validation in local data

## Key Results

* The CCA approach produced higher recognition accuracy than a widely used FFT-based spectrum estimation method.
* The PDF includes a duplicate-publication notice and identifies DOI `10.1109/TBME.2006.886577` as the first version of record.

## Limitations

* Early CCA evaluation with limited target/task settings.
* Exact montage and subject count need a second pass before detailed methods writing.
* Does not address calibration-free versus calibration-based CCA variants later compared by Nakanishi 2015.

## Relevance To Current Review

* Core historical method paper for the CCA branch of SSVEP decoding.
* Provides the predecessor to FBCCA, TRCA comparisons, and modern template/reference methods.

## Evidence Status

| Claim | Status | Evidence Note |
| --- | --- | --- |
| CCA became an important SSVEP frequency recognition method because it uses multichannel EEG and reference signals. | verified | Abstract and methods describe CCA for SSVEP frequency components. |
| CCA outperformed an FFT-based method in this paper. | verified | Abstract states recognition results were higher than the FFT spectrum-estimation approach. |
| CCA alone solves short-window dynamic target selection. | needs confirmation | The paper does not test dynamic object boxes or robot tasks. |

## Open Questions

* Confirm exact participant count and final channel set.
* Decide whether to cite this as 2006 DOI or 2007 issue year in bibliography formatting.