Probabilistic Common Spatial Patterns for Multichannel EEG Analysis
Name
Brown_Probabilistic Common.pdf
Size
2.31 MB
Format
Adobe PDF
Checksum (MD5)
559685a0030d61fa530e9967bdd18ffc
Author(s)
Wu, Wei
Chen, Zhe
Gao, Xiaorong
Li, Yuanqing
Brown, Emery N.
Gao, Shangkai
Date Issued
February 2015
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wu, Wei, Zhe Chen, Xiaorong Gao, Yuanqing Li, Emery N. Brown, and Shangkai Gao. “Probabilistic Common Spatial Patterns for Multichannel EEG Analysis.” IEEE Trans. Pattern Anal. Mach. Intell. 37, no. 3 (March 1, 2015): 639–653.
Version
Author's final manuscript
Abstract
Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tpami.2014.2330598
