Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2144-2148 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728145693 |
| DOIs | |
| Publication status | Published - 2019 Oct |
| Event | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy Duration: 2019 Oct 6 → 2019 Oct 9 |
Publication series
| Name | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
|---|---|
| Volume | 2019-October |
| ISSN (Print) | 1062-922X |
Conference
| Conference | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 |
|---|---|
| Country | Italy |
| City | Bari |
| Period | 19/10/6 → 19/10/9 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction
Cite this
Classification of the motion artifacts in near-infrared spectroscopy based on wavelet statistical feature. / Lee, Seung Bo; Kim, Hakseung; Lee, Seho; Kim, Hyun Ji; Lee, Seong Whan; Kim, Dong Joo.
2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2144-2148 8914331 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2019-October).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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
UR - http://www.scopus.com/inward/citedby.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.
ER -