EEG signal classification using time-varying autoregressive models and common spatial patterns
作者： D. Gutierrez , R. Salazar-Varas
DOI: 10.1109/IEMBS.2011.6091624
关键词:
摘要: The performance of EEG signal classification methods based on Common Spatial Patterns (CSP) depends the operational frequency bands events to be discriminated. This problem has been recently addressed by using a sub-band decomposition signals through filter banks. Even though this approach proven effective, still number filters that are stacked and criteria used determine their cutoff frequencies. Therefore, we propose an alternative eigenstructure signals' time-varying autoregressive (TVAR) models. eigen-based TVAR representation allows for subject-specific estimation principal frequencies, then such eigencomponents can in traditional CSP-based classification. A series simulations show proposed scheme achieve high rates under realistic conditions, as low signal-to-noise ratio (SNR), reduced training experiments, sensors measurements.
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