# SSVEP-005: A benchmark dataset for SSVEP-based brain-computer interfaces > 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-005.md` ## 论文访问 * 内部 PDF: 下载论文 PDF * DOI / 官方页面: [10.1109/TNSRE.2016.2627556](https://doi.org/10.1109/TNSRE.2016.2627556) * 部署边界: 这些 PDF 链接只适合私有/受保护的内部 wiki。 ## SSVEP-005: A benchmark dataset for SSVEP-based brain-computer interfaces ## Metadata * ID: SSVEP-005 * Title: A benchmark dataset for SSVEP-based brain-computer interfaces * Year: 2017 * DOI / URL: 10.1109/TNSRE.2016.2627556 * 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: offline 40-target cue-guided SSVEP speller dataset and benchmark validation * Participants or dataset: 35 healthy subjects, including 8 experienced and 27 naive SSVEP-BCI users * Hardware: 23.6-inch 60 Hz LCD stimulus display; Synamps2 EEG system; dimly lit soundproof room * Channels or sensors: 64-channel whole-head EEG using an extended 10-20 montage, Cz reference, 1000 Hz recording, downsampled to 250 Hz for released epochs ## Methods * Paradigm: 5 x 8 virtual keyboard with 40 flickering targets encoded by joint frequency and phase modulation * Signal processing or model: benchmark analyses include CCA and FBCCA target identification; JFPM simulation is used for stimulus-coding validation * Training/calibration: six blocks per subject, 40 randomized trials per block; supervised methods can use leave-one-block-out cross-validation * Online/offline: offline dataset; the authors explicitly identify online experiments as future dataset extension ## Results * Metrics: classification accuracy and information transfer rate * Main findings: the dataset contains 64 x 1500 x 40 x 6 EEG arrays per subject; FBCCA outperformed CCA in 40-class target identification; JFPM simulation produced high ITR estimates under short target windows * Reported limitations: gaze-dependent speller; realistic free-spelling gaze-shift time may differ from theoretical assumptions; no online BCI data in this dataset; session-transfer and online/offline correspondence remain future work ## Relevance To This Project * Supports: SSVEP benchmark, multi-target frequency/phase coding, short-window decoder evaluation, and channel/electrode-ablation planning * Conflicts with: does not test object-bound dynamic stimuli, AR overlays, robot control, or scene-aware target boxes * Design implication: use this as an offline SSVEP decoder and channel-ablation reference, but keep dynamic YOLO-box SSVEP performance as a project experiment rather than a literature fact ## Extracted Evidence | Claim | Status | Evidence Note | Page/Section | | --- | --- | --- | --- | | The benchmark dataset contains 40-target SSVEP trials from 35 healthy subjects. | verified | The abstract and Methods describe 35 subjects, 40 flickers, six blocks of 40 trials, and 5 s stimulation periods. | Abstract; Sections II.A-II.C | | The dataset provides 64-channel EEG suitable for electrode/channel selection analysis. | verified | Data acquisition used 64 electrodes and the released data matrix includes 64 channels; the discussion explicitly notes that electrode number and locations can be optimized with the dataset. | Sections II.D, III.A, V | | Frequency and phase coding cover 40 targets from 8 Hz to 15.8 Hz with 0.2 Hz spacing. | verified | The stimulus presentation section defines the JFPM coding and phase interval. | Section II.B | | FBCCA is a stronger benchmark decoder than standard CCA on this dataset. | verified | The technical validation reports FBCCA significantly outperforming CCA, with peak ITRs of 68.99 bits/min using 2 s gaze shift and 117.75 bits/min using 0.55 s gaze shift. | Section IV.C | | The dataset can support SAH-BRI-Grasp low-channel and time-window ablations. | inferred | The paper provides 64-channel whole-head data and analyses over data lengths, but it does not itself evaluate the SAH-BRI-Grasp interface. | Sections III.A, IV.C, V | | Dynamic object-box SSVEP selection remains unverified by this paper. | needs confirmation | The task is a fixed gaze-dependent speller, not moving object boxes or AR/robot overlays. | Sections II.B-II.C; V | ## Open Questions * Which subset of occipital/parietal channels should be used for the first SAH-BRI-Grasp low-channel experiment? * How much target-box motion can be tolerated before SSVEP decoding degrades in the project interface? * Should Exp1 report ITR with both practical gaze/attention shift time and raw EEG window time?