TY - JFULL AU - Sajjad Farashi and Mohammadjavad Abolhassani and Mostafa Taghavi Kani PY - 2014/2/ TI - An Empirical Mode Decomposition Based Method for Action Potential Detection in Neural Raw Data T2 - International Journal of Medical and Health Sciences SP - 44 EP - 49 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/9997436 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 85, 2014 N2 - Information in the nervous system is coded as firing patterns of electrical signals called action potential or spike so an essential step in analysis of neural mechanism is detection of action potentials embedded in the neural data. There are several methods proposed in the literature for such a purpose. In this paper a novel method based on empirical mode decomposition (EMD) has been developed. EMD is a decomposition method that extracts oscillations with different frequency range in a waveform. The method is adaptive and no a-priori knowledge about data or parameter adjusting is needed in it. The results for simulated data indicate that proposed method is comparable with wavelet based methods for spike detection. For neural signals with signal-to-noise ratio near 3 proposed methods is capable to detect more than 95% of action potentials accurately. ER -