注意 · GitHub
m-granat/cnn-for-eeg-tsc
Electroencephalography (EEG) is an effective and non-invasive way to capture electric activity of the brain. EEG time series classification is a very important problem in neuroscience as a lot of EEG practical applications, such as medical diagnostics and brain–computer interfaces, depend on the quality of classification results. Several effective classification methods that use deep neural networks were developed in recent years, and convolutional neural networks (CNN) have shown exceptional re
github_m_granat_cnn_for_eeg_tscEEG开放
信息覆盖度
11/ 100
覆盖度反映信息完整程度,不代表信号质量。持续补全中。
核心元数据未知
原始信号未知
事件标记未知
行为标签可推断
受试者规模未知
论文关联可推断
Benchmark未知
跨受试者未知
采集设备未知
Benchmark 证据
| 模型 | 任务 | 类型 | Accuracy |
|---|---|---|---|
| 暂无 Benchmark 证据 | |||
知识图谱
任务
Attention论文
暂无算法
LDARFSVM应用
Attention research相似数据集
OpenNeuro ds00178761.9 · exact task-category matchDriver fatigue EEG dataset58.7 · exact task-category matchChris-Winnard/DAAMEE-experiment46.0 · exact task match评分构成
数据质量0.0
标签质量0.0
科研价值0.0
AI 可用性0.0