Windowed State Space Filters for Peak Interference Suppression in Neural Spike Sorting

Baeriswyl, Christof; Bertrand, Alexander; Wildhaber, Reto A. (2 September 2022). Windowed State Space Filters for Peak Interference Suppression in Neural Spike Sorting In: 30th European Signal Processing Conference EUSIPCO 2022. Belgrade, Serbia. 29 AUG - 2 SEP 2022.

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Signal-to-peak-interference ratio (SPIR) optimal filters are template matching filters with peak interference suppression properties. Such max-SPIR filters are used in multi-pattern recognition problems, such as neural spike sorting in micro-electrode array probes, where cellular action potentials need to be detected and clustered according to their firing neuron cells. In high-density probes with hundreds of channels, such max-SPIR filter banks can require unacceptable high computational resources, in particular for applications with real-time demands and/or on-probe spike sorting. In this paper, we present a computationally attractive substitute for max-SPIR filters by recursively computed Autonomous Linear State Space Model (ALSSM) filters. In our approach, we approximate the impulse response of max-SPIR filters by low order ALSSMs and perform the signal convolution in the new, low-dimensional ALSSM vector space. We demonstrate our method on real neural recordings from high-density probes and show only minimal loss in detection quality while the computational complexity drops by up to a factor 10.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	School of Engineering and Computer Science > Institute for Human Centered Engineering (HUCE) > HUCE / Laboratory for Microelectronics and Medical Devices School of Engineering and Computer Science
Name:	Baeriswyl, Christof; Bertrand, Alexander and Wildhaber, Reto A.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Language:	English
Submitter:	Christof Baeriswyl
Date Deposited:	21 Nov 2022 15:17
Last Modified:	21 Nov 2022 15:17
Uncontrolled Keywords:	multi-class pattern recognition, template matching, linear state space models, neural spike sorting
ARBOR DOI:	10.24451/arbor.18001
URI:	https://arbor.bfh.ch/id/eprint/18001