TY - JOUR
T1 - A Novel EEG Correlation Coefficient Feature Extraction Approach Based on Demixing EEG Channel Pairs for Cognitive Task Classification
AU - Park, Yongkoo
AU - Chung, Wonzoo
N1 - Funding Information:
This work was partly supported by Institute of Information and Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)] and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - This paper presents a novel feature extraction method for electroencephalogram (EEG)-based cognitive task classification based on the correlation coefficients of EEG channel pairs by introducing preprocessing of the EEG signals. The preprocessing attempts to optimally demix each pair of EEG channels using a two-dimensional rotation matrix in order to mitigate the interference between channel pairs and, consequently, to enhance the resulting correlation coefficient features for cognitive task classification. For the optimization, the following criteria are proposed with an optimal rotation angle approximated for each criterion: i ) maximum inter-class correlation coefficient distance (ICCD); ii ) minimum within-class correlation coefficient distance (WCCD); and iii ) maximum Fisher ratio (FR), which is the ratio of ICCD to WCCD. Performance evaluation based on the cognitive task dataset, dataset IV and Ib in BCI competition II, and Keirn and Aunon's dataset, shows that ICCD optimization with the 'above the mean' and 1.5 interquartile range (IQR) feature selection method yields the best classification performance in comparison with other existing cognitive task classification methods.
AB - This paper presents a novel feature extraction method for electroencephalogram (EEG)-based cognitive task classification based on the correlation coefficients of EEG channel pairs by introducing preprocessing of the EEG signals. The preprocessing attempts to optimally demix each pair of EEG channels using a two-dimensional rotation matrix in order to mitigate the interference between channel pairs and, consequently, to enhance the resulting correlation coefficient features for cognitive task classification. For the optimization, the following criteria are proposed with an optimal rotation angle approximated for each criterion: i ) maximum inter-class correlation coefficient distance (ICCD); ii ) minimum within-class correlation coefficient distance (WCCD); and iii ) maximum Fisher ratio (FR), which is the ratio of ICCD to WCCD. Performance evaluation based on the cognitive task dataset, dataset IV and Ib in BCI competition II, and Keirn and Aunon's dataset, shows that ICCD optimization with the 'above the mean' and 1.5 interquartile range (IQR) feature selection method yields the best classification performance in comparison with other existing cognitive task classification methods.
KW - brain-computer interfaces (BCIs)
KW - cognitive task classification
KW - correlation coefficient
KW - electroencephalography (EEG)
UR - http://www.scopus.com/inward/record.url?scp=85085192280&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2993318
DO - 10.1109/ACCESS.2020.2993318
M3 - Article
AN - SCOPUS:85085192280
SN - 2169-3536
VL - 8
SP - 87422
EP - 87433
JO - IEEE Access
JF - IEEE Access
M1 - 9090186
ER -