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) |
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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 |