Bayesian Multi-task Learning for Common Spatial Patterns

Common spatial pattern (CSP) is a widely-used feature extraction method for electroencephalogram (EEG)classification and corresponding probabilistic models were recently developed, adopting a linear generative model for each class. These models are trained on a subject-by-subject basis so that inter...

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Bibliographic Details
Main Authors: Hyohyeong Kang, Seungjin Choi
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Bayesian multi-task learning
brain computer interface
Brain models
common spatial patterns
Computational modeling
Electroencephalography
Probabilistic logic
Training
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Summary:Common spatial pattern (CSP) is a widely-used feature extraction method for electroencephalogram (EEG)classification and corresponding probabilistic models were recently developed, adopting a linear generative model for each class. These models are trained on a subject-by-subject basis so that inter-subject information is neglected. Moreover when only a few training samples are available for each subject, the performance is degraded. In this paper we employ Bayesian multi-task learning so that subject-to-subject information is transferred in learning the model for a subject of interest. We present two probabilistic models where precision parameters of multivariate or matrix-variate Gaussian prior for the dictionary are shared across subjects. Numerical experiments on the BCI competition IV 2a dataset confirm that our methods improve classification performance over the standard CSP (on a subject-by-subject basis), especially in the case of subjects with fewer number of training samples.
DOI:10.1109/PRNI.2011.8