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Comprehensive common spatial patterns with temporal structure information of EEG data : minimizing nontask related EEG component

หน่วยงาน Nanyang Technological University, Singapore

รายละเอียด

ชื่อเรื่อง : Comprehensive common spatial patterns with temporal structure information of EEG data : minimizing nontask related EEG component
นักวิจัย : Wang, Haixian , Xu, Dong
คำค้น : DRNTU::Engineering::Computer science and engineering.
หน่วยงาน : Nanyang Technological University, Singapore
ผู้ร่วมงาน : -
ปีพิมพ์ : 2555
อ้างอิง : Wang, H., & Xu, D. (2012). Comprehensive common spatial patterns with temporal structure information of EEG data: Minimizing nontask related EEG component. IEEE Transactions on Biomedical Engineering, 59(9), 2496-2505. , http://hdl.handle.net/10220/11253 , http://dx.doi.org/10.1109/TBME.2012.2205383
ที่มา : -
ความเชี่ยวชาญ : -
ความสัมพันธ์ : IEEE transactions on biomedical engineering
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

In the context of electroencephalogram (EEG)-based brain-computer interfaces (BCI), common spatial patterns (CSP) is widely used for spatially filtering multichannel EEG signals. CSP is a supervised learning technique depending on only labeled trials. Its generalization performance deteriorates due to overfitting occurred when the number of training trials is small. On the other hand, a large number of unlabeled trials are relatively easy to obtain. In this paper, we contribute a comprehensive learning scheme of CSP (cCSP) that learns on both labeled and unlabeled trials. cCSP regularizes the objective function of CSP by preserving the temporal relationship among samples of unlabeled trials in terms of linear representation. The intrinsically temporal structure is characterized by an $ell_1$ graph. As a result, the temporal correlation information of unlabeled trials is incorporated into CSP, yielding enhanced generalization capacity. Interestingly, the regularizer of cCSP can be interpreted as minimizing a nontask related EEG component, which helps cCSP alleviate nonstationarities. Experiment results of single-trial EEG classification on publicly available EEG datasets confirm the effectiveness of the proposed method.

บรรณานุกรม :
Wang, Haixian , Xu, Dong . (2555). Comprehensive common spatial patterns with temporal structure information of EEG data : minimizing nontask related EEG component.
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Wang, Haixian , Xu, Dong . 2555. "Comprehensive common spatial patterns with temporal structure information of EEG data : minimizing nontask related EEG component".
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Wang, Haixian , Xu, Dong . "Comprehensive common spatial patterns with temporal structure information of EEG data : minimizing nontask related EEG component."
    กรุงเทพมหานคร : Nanyang Technological University, Singapore, 2555. Print.
Wang, Haixian , Xu, Dong . Comprehensive common spatial patterns with temporal structure information of EEG data : minimizing nontask related EEG component. กรุงเทพมหานคร : Nanyang Technological University, Singapore; 2555.