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

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

DOI:
10.3791/64959-v

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06:09 min

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September 08, 2023

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, , , , ,
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

  • 00:00Introduction
  • 00:27Installing and Configuring the CBLE Performance Estimation Graphical User Interface
  • 01:33Data Splitting, Model Training, and Accuracy Evaluation for BrainInvaders Dataset
  • 03:05Data Splitting and Model Training for BCI2000 Dataset
  • 04:28Results I: Analysis of BrainInvaders Dataset Using vCBLE as a Predictor of BCI Accuracy
  • 05:07Results II: Analysis of Michigan Dataset Using vCBLE as a Predictor of BCI Accuracy
  • 05:43Conclusion

Summary

Automatic Translation

Automatic Translation

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

Tags

Keywords: P300Brain-computer InterfaceSpellerPerformance EstimationClassifier-based Latency EstimationCBLEAccuracy PredictionEEG DatasetBrain InvaderLinear RegressionRMSEVCBLE

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