A Modified Common Spatial Pattern Algorithm Customized for Feature Dimensionality Reduction in fNIRS-Based BCIs

Functional near-infrared spectroscopy (fNIRS) is a non-invasive multi-channel imaging tool for assessing brain activities, which has shown its high potential in brain-computer interface (BCI) technique. Most previous studies have focused on constructing high dimensional features from whole channels,...

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Published in2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2018; pp. 5073 - 5076
Main Authors Jiang, Xinyu, Gu, Xiao, Mei, Zhenning, Ren, Haoran, Chen, Wei
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2018
Subjects
Classification algorithms
Electroencephalography
Feature extraction
Filtering algorithms
Hemodynamics
Spatial filters
Task analysis
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Summary:Functional near-infrared spectroscopy (fNIRS) is a non-invasive multi-channel imaging tool for assessing brain activities, which has shown its high potential in brain-computer interface (BCI) technique. Most previous studies have focused on constructing high dimensional features from whole channels, adding to the complexity of their classifiers. Another multi-channel source for BCI is electroencephalograph (EEG), which possesses different spatial and temporal features from fNIRS. In EEG field, Common Spatial Pattern (CSP) algorithm is widely used aimed at dimensionality reduction. In our article, we modified it based on the characteristics of fNIRS and evaluated its effectiveness in discriminating Mental Arithmetic (MA) against resting status in an open-access dataset. The Modified Common Spatial Pattern algorithm significantly outperforms CSP algorithm in fNIRS-based BCI and shows its potential in further BCI related explorations.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2018.8513454