Probabilistic Models for Common Spatial Patterns: Parameter-Expanded EM and Variational Bayes
| Autor: | Hyohyeong Kang, Seungjin Choi |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 26:970-976 |
| ISSN: | 2374-3468 2159-5399 |
| Popis: | Common spatial patterns (CSP) is a popular feature extraction method for discriminating between positive andnegative classes in electroencephalography (EEG) data.Two probabilistic models for CSP were recently developed: probabilistic CSP (PCSP), which is trained by expectation maximization (EM), and variational BayesianCSP (VBCSP) which is learned by variational approx-imation. Parameter expansion methods use auxiliaryparameters to speed up the convergence of EM or thedeterministic approximation of the target distributionin variational inference. In this paper, we describethe development of parameter-expanded algorithms forPCSP and VBCSP, leading to PCSP-PX and VBCSP-PX, whose convergence speed-up and high performanceare emphasized. The convergence speed-up in PCSP-PX and VBCSP-PX is a direct consequence of parame-ter expansion methods. The contribution of this study is the performance improvement in the case of CSP,which is a novel development. Numerical experimentson the BCI competition datasets, III IV a and IV 2ademonstrate the high performance and fast convergenceof PCSP-PX and VBCSP-PX, as compared to PCSP andVBCSP. |
| Databáze: | OpenAIRE |
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