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Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning

Zamani, M; Sokolic, J; Jiang, D; Renna, F; Rodrigues, M; Demosthenous, A; (2020) Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning. IEEE Transactions on Biomedical Circuits and Systems 10.1109/TBCAS.2020.2969910. (In press). Green open access

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Abstract

This paper presents an adaptable dictionary-based feature extraction approach for spike sorting offering high accuracy and low computational complexity for implantable applications. It extracts and learns identifiable features from evolving subspaces through matched unsupervised subspace filtering. To provide compatibility with the strict constraints in implantable devices such as the chip area and power budget, the dictionary contains arrays of {-1, 0 and 1} and the algorithm need only process addition and subtraction operations. Three types of such dictionary were considered. To quantify and compare the performance of the resulting three feature extractors with existing systems, a neural signal simulator based on several different libraries was developed. For noise levels $\sigma_N$ between 0.05 and 0.3 and groups of 3 to 6 clusters, all three feature extractors provide robust high performance with average classification errors of less than 8% over five iterations, each consisting of 100 generated data segments. To our knowledge, the proposed adaptive feature extractors are the first able to classify reliably 6 clusters for implantable applications. An ASIC implementation of the best performing dictionary-based feature extractor was synthesized in a 65-nm CMOS process. It occupies an area of 0.09 mm2 and dissipates up to about 10.48 μW from a 1 V supply voltage, when operating with 8-bit resolution at 30 kHz operating frequency.

Type: Article
Title: Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TBCAS.2020.2969910
Publisher version: https://doi.org/10.1109/TBCAS.2020.2969910
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Complexity optimization, digital ASIC, feature extraction, implantable devices, high performance classification, spike sorting, subspace tracking, unsupervised learning.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10091557
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