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Open-Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping

Authors
Bak, SuJinPark, JinwooShin, JaeyoungJeong, Jichai
Issue Date
12월-2019
Publisher
MDPI
Keywords
brain-computer interfaces; functional near-infrared spectroscopy; open-access dataset; finger-tapping; foot-tapping; three-class
Citation
ELECTRONICS, v.8, no.12
Indexed
SCIE
SCOPUS
Journal Title
ELECTRONICS
Volume
8
Number
12
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/61475
DOI
10.3390/electronics8121486
ISSN
2079-9292
Abstract
Numerous open-access electroencephalography (EEG) datasets have been released and widely employed by EEG researchers. However, not many functional near-infrared spectroscopy (fNIRS) datasets are publicly available. More fNIRS datasets need to be freely accessible in order to facilitate fNIRS studies. Toward this end, we introduce an open-access fNIRS dataset for three-class classification. The concentration changes of oxygenated and reduced hemoglobin were measured, while 30 volunteers repeated each of the three types of overt movements (i.e., left- and right-hand unilateral complex finger-tapping, foot-tapping) for 25 times. The ternary support vector machine (SVM) classification accuracy obtained using leave-one-out cross-validation was estimated at 70.4% +/- 18.4% on average. A total of 21 out of 30 volunteers scored a superior binary SVM classification accuracy (left-hand vs. right-hand finger-tapping) of over 80.0%. We believe that the introduced fNIRS dataset can facilitate future fNIRS studies.
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