SOURCE RECORD / S0200
Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning
Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning
- Authors or organization
- Hu L, Gao W, Lu Z, Shan C, Ma H, Zhang W, Li Y
- Published date
- 2024-09-25
- Source type
- peer_reviewed_human_dataset_study
- Technology route
- non-invasive wearable EEG P300
- Function or setting
- assistive spelling and low-calibration BCI
- Rights status
- manual_review / CC BY-NC-ND 4.0 (association not snapshotted)
Legacy aliases
—
Related topics
- BCI 最优方案
- 非侵入式 BCI
Evidence summary
Evidence: Subject-independent and fine-tuned wearable P300 feasibility. Key figures: 73.23+/-7.62% without calibration; 78.75+/-6.37% with fine-tuning. Limitations: Accuracy is not characters per minute or errors per hour; patient, cross-day, home-use, product availability and China TCO evidence not reported.
Key figures
73.23+/-7.62% without calibration; 78.75+/-6.37% with fine-tuning
Limitations
Accuracy is not characters per minute or errors per hour; patient, cross-day, home-use, product availability and China TCO evidence not reported
This source currently has a local metadata record only. No public mirror is provided because redistribution permission has not been confirmed; use the original source above for verification.