Abstract:
Feature extraction is a critical step in real-time spike sorting after a spike is detected. Features should be informative and noise insensitive for high classification a...Show MoreMetadata
Abstract:
Feature extraction is a critical step in real-time spike sorting after a spike is detected. Features should be informative and noise insensitive for high classification accuracy. This paper describes a new feature extraction method that utilizes a feature denoising filter to improve noise immunity while preserving spike information. Six features were extracted from filtered spikes, including a newly developed feature, and a separability index was applied to select optimal features. Using a set of the three highest-performing features, which includes the new feature, this method can achieve spike classification error as low as 5% for the worst case noise level of 0.2. The computational complexity is only 11% of principle component analysis method and it only costs nine registers per channel.
Published in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Date of Conference: 26-30 August 2014
Date Added to IEEE Xplore: 06 November 2014
Electronic ISBN:978-1-4244-7929-0
ISSN Information:
PubMed ID: 25570192