P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
470 Views
•
06:09 min
•
September 8th, 2023
DOI :
September 8th, 2023
•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
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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