A Python Code for Maximum Likelihood Estimation of the Location and Scale Parameters of the Truncated Normal Distribution

Melih Yilmaz Ogutcen, Mehmet Kocaturk, Murat Okatan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Extracellular neural recordings obtained from chronically implanted microelectrode arrays are widely used in behavioral neurophysiology and invasive brain-machine interfaces. After the raw recordings are band-pass filtered within a frequency band suitable for spike detection, spikes are often detected by amplitude thresholding. Developing principled methods for computing amplitude thresholds is an active research area. 'Truncation thresholds' are a pair of amplitude thresholds that are computed using a recently proposed algorithm. As part of an effort that aims to integrate this algorithm into a real-Time data acquisition and spike detection system, here we present a Python code for maximum likelihood estimation of the location and scale parameters of the truncated Normal distribution, which is one of the steps involved in the computation of truncation thresholds.

Original languageEnglish
Title of host publicationTIPTEKNO 2021 - Tip Teknolojileri Kongresi - 2021 Medical Technologies Congress
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665436632
DOIs
Publication statusPublished - 2021
Event2021 Medical Technologies Congress, TIPTEKNO 2021 - Antalya, Turkey
Duration: 4 Nov 20216 Nov 2021

Publication series

NameTIPTEKNO 2021 - Tip Teknolojileri Kongresi - 2021 Medical Technologies Congress

Conference

Conference2021 Medical Technologies Congress, TIPTEKNO 2021
Country/TerritoryTurkey
CityAntalya
Period4/11/216/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • maximum likelihood estimation
  • python code
  • truncated Normal distribution
  • truncation thresholds

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