tailieunhanh - Báo cáo hóa học: " Research Article An Unsupervised and Drift-Adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Unsupervised and Drift-Adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 696741 13 pages doi 2011 696741 Research Article An Unsupervised and Drift-Adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming Michal Natora and Klaus Obermayer Institute for Software Engineering and Theoretical Computer Science Faculty IV Berlin Institute of Technology TU Berlin Franklinstrafe 28 29 10623 Berlin Germany Correspondence should be addressed to Michal Natora natora@ Received 15 June 2010 Revised 25 October 2010 Accepted 16 November 2010 Academic Editor Raviraj S. Adve Copyright 2011 M. Natora and K. Obermayer. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. In the case of extracellular recordings spike detection algorithms are necessary in order to retrieve information about neuronal activity from the data. We present a new spike detection algorithm which is based on methods from the field of blind equalization and beamforming and which is particularly adapted to the specific signal structure neuronal data exhibit. In contrast to existing approaches our method blindly estimates several waveforms directly from the data selects automatically an appropriate detection threshold and is also able to track neurons by filter adaptation. The few parameters of the algorithm are biologically motivated thus easy to set. We compare our method with current state-of-the-art spike detection algorithms and show that the proposed method achieves favorable results. Realistically simulated data as well as data acquired from simultaneous intra extracellular recordings in rat slices are used as evaluation datasets. 1. Introduction Extracellular recordings with electrodes constitute one of the main techniques for acquiring data from the central nervous system in order to .