Using Johnson's SU Distribution for Modeling the Background Activity in Extracellular Neural Recordings

Melih Yilmaz Ogutcen, Mehmet Kocaturk, Murat Okatan

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

Extracellular neural recordings obtained from awake behaving subjects through chronically implanted microelectrode arrays provide information about the functioning of the brain with sub-millisecond temporal resolution at the level of individual neurons. After bandpass filtering in a frequency range suitable for spike detection, these recordings consist of spikes and background activity. Methods exist to segment the background activity automatically using truncation thresholds and Otsu-based methods. In previous work, truncation thresholds have been computed using the truncated Normal distribution. Here, we use the truncated Johnson's SU distribution instead to examine whether it segments the background activity better. We also find that the truncated Johnson's SU distribution explains the background activity segmented by Otsu-based thresholds. These results are useful for developing invasive brain-computer-interfaces that automatically extract information from extracellular neural recordings in real time.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıTIPTEKNO 2021 - Tip Teknolojileri Kongresi - 2021 Medical Technologies Congress
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665436632
DOI'lar
Yayın durumuYayınlandı - 2021
Etkinlik2021 Medical Technologies Congress, TIPTEKNO 2021 - Antalya, Turkey
Süre: 4 Kas 20216 Kas 2021

Yayın serisi

AdıTIPTEKNO 2021 - Tip Teknolojileri Kongresi - 2021 Medical Technologies Congress

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???event.eventtypes.event.conference???2021 Medical Technologies Congress, TIPTEKNO 2021
Ülke/BölgeTurkey
ŞehirAntalya
Periyot4/11/216/11/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Finansman

This work was supported by Research Fund of the Istanbul Technical University. Project Number: MAB-2020-42808.

FinansörlerFinansör numarası
Istanbul Teknik ÜniversitesiMAB-2020-42808

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