NDLI: Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating

Content Provider	frontiers
Author	Shin, Jaeyoung Im, Chang-Hwan
Abstract	Ensemble classifiers have been proven to result in better classification accuracy than that of a single strong learner in many machine learning studies. Although many studies on electroencephalography-brain-computer interface (BCI) used ensemble classifiers to enhance the BCI performance, ensemble classifiers have hardly been employed for near-infrared spectroscopy (NIRS)-BCIs. In addition, since there has not been any systematic and comparative study, the efficacy of ensemble classifiers for NIRS-BCIs remains unknown. In this study, four NIRS-BCI datasets were employed to evaluate the efficacy of linear discriminant analysis ensemble classifiers based on the bootstrap aggregating. From the analysis results, significant (or marginally significant) increases in the bitrate as well as the classification accuracy were found for all four NIRS-BCI datasets employed in this study. Moreover, significant bitrate improvements were found in two of the four datasets.
ISSN	1662453X
DOI	10.3389/fnins.2020.00168
Volume Number	14
Journal	Frontiers in Neuroscience
Language	English
Publisher Date	2020-03-04
Access Restriction	Open
Subject Keyword	Bootstrap aggregating Ensemble learning Pattern Classification Functional near inferred spectroscopy Brain-computer interface (BCI)
Content Type	Text
Resource Type	Article
Subject	Neuroscience

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Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating