TY - JOUR
T1 - A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
AU - Chen, Yan
AU - Hang, Wenlong
AU - Liang, Shuang
AU - Liu, Xuejun
AU - Li, Guanglin
AU - Wang, Qiong
AU - Qin, Jing
AU - Choi, Kup Sze
N1 - Funding Information:
This work was supported in part by the Key R&D Program of Guangdong Province, China under Grant (2018B030339001), the National Natural Science Foundation of China under Grants (61802177 and 62072452), the Fundamental Research Funds for the Central Universities under Grant (CDLS-2019-04), the CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems (2014DP173025), and the Hong Kong Research Grants Council under Grant (PolyU 152006/19E).
Publisher Copyright:
© Copyright © 2020 Chen, Hang, Liang, Liu, Li, Wang, Qin and Choi.
PY - 2020/11/23
Y1 - 2020/11/23
N2 - In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.
AB - In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.
KW - brain-computer interface
KW - electroencephalography
KW - motor imagery
KW - support matrix machine
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85097298130&partnerID=8YFLogxK
U2 - 10.3389/fnins.2020.606949
DO - 10.3389/fnins.2020.606949
M3 - Journal article
AN - SCOPUS:85097298130
SN - 1662-4548
VL - 14
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 606949
ER -