Linear dynamic models for classification of single-trial EEG
This paper investigates the use of linear dynamic models (LDMs) to improve classification of single-trial EEG signals. Existing dynamic classification of EEG uses discrete-state hidden Markov models (HMMs) based on piecewise-stationary assumption, which is inadequate for modeling the highly non-stat...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2013
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| Subjects: |