A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation
Joint Authors
Hasan, Mustafa A. H.
Khan, Muhammad U.
Mishra, Deepti
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-19
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS).
EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification.
This integration helps to achieve better system performance, but at the cost of an increase in system complexity and computational time.
In hybrid BCI studies, channel selection is recognized as the key element that directly affects the system’s performance.
In this paper, we propose a novel channel selection approach using the Pearson product-moment correlation coefficient, where only highly correlated channels are selected from each hemisphere.
Then, four different statistical features are extracted, and their different combinations are used for the classification through KNN and Tree classifiers.
As far as we know, there is no report available that explored the Pearson product-moment correlation coefficient for hybrid EEG-fNIRS BCI channel selection.
The results demonstrate that our hybrid system significantly reduces computational burden while achieving a classification accuracy with high reliability comparable to the existing literature.
American Psychological Association (APA)
Hasan, Mustafa A. H.& Khan, Muhammad U.& Mishra, Deepti. 2020. A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation. BioMed Research International،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1131995
Modern Language Association (MLA)
Hasan, Mustafa A. H.…[et al.]. A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation. BioMed Research International No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1131995
American Medical Association (AMA)
Hasan, Mustafa A. H.& Khan, Muhammad U.& Mishra, Deepti. A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1131995
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131995