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Computationally efficient feature denoising filter and selection of optimal features for noise insensitive spike sorting | IEEE Conference Publication | IEEE Xplore

Computationally efficient feature denoising filter and selection of optimal features for noise insensitive spike sorting


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 More

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.
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
Conference Location: Chicago, IL, USA

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