基于分类器的潜伏期估计对P300脑机接口拼写系统性能的评估
1.3K 次观看
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被引用 1 次
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06:09 分钟
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2023年9月8日
DOI :10.3791/64959-v
2023年9月8日
•1.3K 次观看
Nazmun N. Khan1 , Taylor Sweet1 , Chase A. Harvey1 , Seth Warschausky2 , Jane E. Huggins3,4 , David E. Thompson1
1Brain and Body Sensing Lab, Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, 2Adaptive Cognitive Assessment Laboratory, Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, 3Direct Brain Interface Laboratory, Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, 4Direct Brain Interface Laboratory, Department of Biomedical Engineering, University of Michigan, Ann Arbor
本文介绍了一种利用小型测试数据集评估同一天P300拼写脑-机接口（BCI）准确性的方法。
Chapters in this video
0:00
Introduction
0:27
Installing and Configuring the CBLE Performance Estimation Graphical User Interface
1:33
Data Splitting, Model Training, and Accuracy Evaluation for BrainInvaders Dataset
3:05
Data Splitting and Model Training for BCI2000 Dataset
4:28
Results I: Analysis of BrainInvaders Dataset Using vCBLE as a Predictor of BCI Accuracy
5:07
Results II: Analysis of Michigan Dataset Using vCBLE as a Predictor of BCI Accuracy
5:43
Conclusion
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