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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Article DOI: 10.3791/64959-v • 06:09 min • September 8th, 2023
September 8th, 2023 •
1, 1, 1, 2, 3,4, 1
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

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This article presents a method for estimating same-day P300 speller Brain-Computer Interface (BCI) accuracy using a small testing dataset.

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P300-Based Brain-Computer Interface Speller Performance Estimation Classifier-based Latency Estimation BCI Systems Data Collection Validation End Users Experimenters Random Variation False Inferences P300 Spellers Accuracy Estimation Time Investment Confidence Bounds Classifier-Based Latency Estimation (CBLE) Protocol Typing Data
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