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Journal
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Neuroscience
/
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
JoVE Journal
Neuroscience
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JoVE Journal
Neuroscience
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Please note that all translations are automatically generated.
Click here for the English version.
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
DOI:
10.3791/64959-v
•
06:09 min
•
September 08, 2023
•
Nazmun N. Khan
,
Taylor Sweet
,
Chase A. Harvey
,
Seth Warschausky
,
Jane E. Huggins
4
,
David E. Thompson
1
Brain and Body Sensing Lab, Mike Wiegers Department of Electrical & Computer Engineering
,
Kansas State University
,
2
Adaptive Cognitive Assessment Laboratory, Department of Physical Medicine and Rehabilitation
,
University of Michigan, Ann Arbor
,
3
Direct Brain Interface Laboratory, Department of Physical Medicine and Rehabilitation
,
University of Michigan, Ann Arbor
,
4
Direct Brain Interface Laboratory, Department of Biomedical Engineering
,
University of Michigan, Ann Arbor
Chapters
00:00
Introduction
00:27
Installing and Configuring the CBLE Performance Estimation Graphical User Interface
01:33
Data Splitting, Model Training, and Accuracy Evaluation for BrainInvaders Dataset
03:05
Data Splitting and Model Training for BCI2000 Dataset
04:28
Results I: Analysis of BrainInvaders Dataset Using vCBLE as a Predictor of BCI Accuracy
05:07
Results II: Analysis of Michigan Dataset Using vCBLE as a Predictor of BCI Accuracy
05:43
Conclusion
Summary
Automatic Translation
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Automatic Translation
本稿では、小規模なテストデータセットを使用して、同日P300スペラーブレイン・コンピューター・インターフェース(BCI)の精度を推定する方法を紹介します。
Tags
P300
Brain-computer Interface
Speller
Performance Estimation
Classifier-based Latency Estimation
CBLE
Accuracy Prediction
EEG Dataset
Brain Invader
Linear Regression
RMSE
VCBLE
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