TY - GEN
T1 - Classification of the motion artifacts in near-infrared spectroscopy based on wavelet statistical feature
AU - Lee, Seung Bo
AU - Kim, Hakseung
AU - Lee, Seho
AU - Kim, Hyun Ji
AU - Lee, Seong Whan
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).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Near-infrared spectroscopy (NIRS) is receiving much attention in the fields of brain-computer interface (BCI) due to its noninvasiveness, usability, and high performance. However, NIRS is susceptible to the motion artifacts, and its high morphological variety hinders the existing signals artifacts elimination techniques from being applied to NIRS. This study proposes a novel feature extraction method for classifying motion artifacts. NIRS data from an open access dataset containing five types of motion artifact (i.e., eye blinking, head movement, eyes movement, teeth clenching, and mouth opening) from 28 healthy subjects were analyzed. The efficacy of the proposed wavelet statistical feature extraction method in artifact classification was compared to various existing feature extraction methods for BCI designs. Each feature was learned by four conventional machine learning models for classifying NIRS motion artifacts. Shrinkage regularized linear discriminant analysis (SRLDA) with the proposed features derived from oxy and deoxy-hemoglobin NIRS signal achieved 90% accuracy and 0.85 Cohen's kappa coefficient for classifying types of motion artifacts. Mann-Whitney U test and paired T-test indicate that SRLDA with the proposed feature had significantly higher classification performance compared to the other models. The proposed method can reliably classify the motion artifacts to standardize the morphology types of NIRS artifact. It could be used to augment signal quality control technique with further extension in future NIRS-based BCI systems.
AB - Near-infrared spectroscopy (NIRS) is receiving much attention in the fields of brain-computer interface (BCI) due to its noninvasiveness, usability, and high performance. However, NIRS is susceptible to the motion artifacts, and its high morphological variety hinders the existing signals artifacts elimination techniques from being applied to NIRS. This study proposes a novel feature extraction method for classifying motion artifacts. NIRS data from an open access dataset containing five types of motion artifact (i.e., eye blinking, head movement, eyes movement, teeth clenching, and mouth opening) from 28 healthy subjects were analyzed. The efficacy of the proposed wavelet statistical feature extraction method in artifact classification was compared to various existing feature extraction methods for BCI designs. Each feature was learned by four conventional machine learning models for classifying NIRS motion artifacts. Shrinkage regularized linear discriminant analysis (SRLDA) with the proposed features derived from oxy and deoxy-hemoglobin NIRS signal achieved 90% accuracy and 0.85 Cohen's kappa coefficient for classifying types of motion artifacts. Mann-Whitney U test and paired T-test indicate that SRLDA with the proposed feature had significantly higher classification performance compared to the other models. The proposed method can reliably classify the motion artifacts to standardize the morphology types of NIRS artifact. It could be used to augment signal quality control technique with further extension in future NIRS-based BCI systems.
UR - http://www.scopus.com/inward/record.url?scp=85076744056&partnerID=8YFLogxK
U2 - 10.1109/SMC.2019.8914331
DO - 10.1109/SMC.2019.8914331
M3 - Conference contribution
AN - SCOPUS:85076744056
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2144
EP - 2148
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -