注意 / GitHub

Brother-Five/spare-beyesian

Brain Computer Interface(BCI) data is transformed and banked by BCI from human brain action to.Sparse Bayesian learning is a machine learning method.Its high efficiency of calculation meets the requirement of the analysis of the Brain Computer Interface Data.Here we use Sparse Bayesian linear Discriminant Analysis(SBDA) and its variant as models to classify the electroencephalogram (EEG) tested in P300 dataset from the BCI competition 2004.Through a series of pre-process including bandpass filte

github_brother_five_spare_beyesianEEGunknown2 labels0 benchmarks

数据资产概览

受试者—

通道—

采样率—

任务p300

许可unknown

可用性open

INTELLIGENCE COVERAGE

数据理解覆盖度

11.2

区分已验证事实、可推断线索与未知信息；覆盖度不是信号质量结论。

核心元数据未知0/5 个核心字段已知

原始信号未知暂无结构化证据

事件标记未知暂无结构化证据

行为标签可推断2 个 Agent 推断标签，需人工核验

受试者规模未知暂无结构化证据

论文关联可推断目录含论文元数据但尚未建立 Repository 关联

Benchmark未知暂无结构化证据

跨受试者未知暂无结构化证据

采集设备未知暂无结构化证据

优先补全动作

验证原始信号文件可访问性
解析并验证事件标记
人工核验行为标签
建立 Dataset–Paper 关联

知识图谱邻域

TASKAttention

PAPER暂无关联

ALGORITHMLDARFSVM

APPLICATIONAttention research

Benchmark 证据

“主任务”直接对应数据集分类；“衍生任务”与“代理 / 探索性”复用同一数据集构造，不应视为原始研究任务。