DocumentCode :
3667473
Title :
An algorithm for movement related potentials feature extraction based on transfer learning
Author :
Peitao Wang;Jun Lu;Chuan Lu;Zeng Tang
Author_Institution :
School of Automation, Guangdong University of Technology, and Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China
fYear :
2015
fDate :
4/1/2015 12:00:00 AM
Firstpage :
309
Lastpage :
314
Abstract :
Movement related potentials (MRPs) are utilized as features in many motor related brain-computer interfaces (BCIs). MRP feature extraction is challenging since multi-channel brain signals are high dimensional and often contains various artifacts. The discriminative spatial pattern (DSP) algorithm successfully improves the signal-to-noise ratio of MRPs. However, abundant labeled training data are required for DSP to learn reliable spatial filters for each subject respectively. This is inconvenient for the applications of BCIs. In this paper, we propose a regularized DSP (RDSP) algorithm for MRP feature extraction, which does not need any labeled training data for a new subject. The regularization function of RDSP is built on empirical maximum mean discrepancy (MMD) to reduce the differences not only in marginal distribution but also in conditional distribution between subjects. RDSP transfers the common discriminative spatial filters across subjects and updates them iteratively by semi-supervised learning. Experiment results on BCI competition datasets show the effectiveness of RDSP.
Keywords :
"Feature extraction","Digital signal processing","Measurement","Materials requirements planning"
Publisher :
ieee
Conference_Titel :
Information Science and Technology (ICIST), 2015 5th International Conference on
Type :
conf
DOI :
10.1109/ICIST.2015.7288988
Filename :
7288988
Link To Document :
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