TY - GEN
T1 - Comparative Analysis of NIRS-EEG Motor Imagery Data Using Features from Spatial, Spectral and Temporal Domain
AU - Kim, Hyun Ji
AU - Wang, In Nea
AU - Kim, Young Tak
AU - Kim, Hakseung
AU - Kim, Dong Joo
N1 - Funding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface); Convergent Technology R&D Program for Human Augmentation through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT (NRF-2019M3C1B8077477).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Brain-computer interface (BCI) systems, which provide users with an additional channel to communicate with external devices, have been mainly developed using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). To complement each modality's pros and cons, various hybrid NIRS-EEG studies have been investigated. However, most studies focused on enhancing the classification accuracy rather than analyzing the characteristics of used features. This study aimed to investigate whether EEG features from spatial, temporal, and spectral domains would exhibit the diverse efficacy in hybrid NIRS-EEG BCI. Open access NIRS and EEG recordings of left/right hand gripping imagery from twenty-nine healthy subjects were utilized. Common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD) were separately employed with NIRS to evaluate the discrimination performance. Within dataset, NIRS with CSP showed the highest classification accuracy with linear support vector machine (LSVM) classifier (mean accuracy, 71.4%). For kernel SVM (KSVM) classifiers, mean accuracy of NIRS with TDP features was lower than accuracy of only NIRS features (mean accuracy, NIRS: 53.1% and NIRS with TDP: 50.5%). The findings suggested that binary motor imagery tasks, which involve localized brain activation, could be enhanced by applying features including rich spatial information.
AB - Brain-computer interface (BCI) systems, which provide users with an additional channel to communicate with external devices, have been mainly developed using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). To complement each modality's pros and cons, various hybrid NIRS-EEG studies have been investigated. However, most studies focused on enhancing the classification accuracy rather than analyzing the characteristics of used features. This study aimed to investigate whether EEG features from spatial, temporal, and spectral domains would exhibit the diverse efficacy in hybrid NIRS-EEG BCI. Open access NIRS and EEG recordings of left/right hand gripping imagery from twenty-nine healthy subjects were utilized. Common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD) were separately employed with NIRS to evaluate the discrimination performance. Within dataset, NIRS with CSP showed the highest classification accuracy with linear support vector machine (LSVM) classifier (mean accuracy, 71.4%). For kernel SVM (KSVM) classifiers, mean accuracy of NIRS with TDP features was lower than accuracy of only NIRS features (mean accuracy, NIRS: 53.1% and NIRS with TDP: 50.5%). The findings suggested that binary motor imagery tasks, which involve localized brain activation, could be enhanced by applying features including rich spatial information.
KW - Brain-computer interface (BCI)
KW - electroencephalography (EEG)
KW - hybrid BCI
KW - motor imagery
KW - near-infrared spectroscopy (NIRS)
UR - http://www.scopus.com/inward/record.url?scp=85084081046&partnerID=8YFLogxK
U2 - 10.1109/BCI48061.2020.9061636
DO - 10.1109/BCI48061.2020.9061636
M3 - Conference contribution
AN - SCOPUS:85084081046
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
Y2 - 26 February 2020 through 28 February 2020
ER -