2nd International Conference on Bio-Inspired Systems and Signal Processing, Oporto, Portugal, 14 - 17 January 2009, pp.115-116
In this paper Empirical Mode Decomposition (EMD)-based features from single-channel electroencephalographic (EEG) data are proposed for rat's sleep state classification. The classification performances of the EMD-based features and some classical power spectrum density (PSD)-based features are compared. Supported by experiments on real EEG data, we demonstrate that classification performances can significantly improve, by simply substituting EMD to PSD in features extraction. This is in noticeably due to the natural adaptivity of EMD which show more robust to subjects variability.