A Low-Power Oscillatory Feature Extraction Unit for Implantable Neural Interfaces

Hoda Yassin; Arash Akhoundi; El Sayed Hasaneen; Dante G. Muratore

doi:10.1109/ISCAS46773.2023.10181914

A Low-Power Oscillatory Feature Extraction Unit for Implantable Neural Interfaces

Hoda Yassin, Arash Akhoundi, El Sayed Hasaneen, Dante G. Muratore

Bio-Electronics

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

63 Downloads (Pure)

Abstract

Power and area efficient on-chip feature extraction is needed for future closed-loop neural interfaces. This paper presents a feature extraction unit for neural oscillatory synchrony that bypasses the phase extraction step to reduce hardware complexity. Instead, the sine and cosine of the phase are directly approximated from the real and imaginary components of the signal to calculate the phase-amplitude coupling (PAC) and phase locking value (PLV). The synthesized design achieves state-of-the-art performances at 43 nW/channel and 0.006 mm2, while maintaining sufficient accuracy for seizure detection in epileptic patients.

Original language	English
Title of host publication	ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9781665451093
DOIs	https://doi.org/10.1109/ISCAS46773.2023.10181914
Publication status	Published - 2023
Event	56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States Duration: 21 May 2023 → 25 May 2023

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2023-May
ISSN (Print)	0271-4310

Conference

Conference	56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
Country/Territory	United States
City	Monterey
Period	21/05/23 → 25/05/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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10.1109/ISCAS46773.2023.10181914

A_Low-Power_Oscillatory_Feature_Extraction_Unit_for_Implantable_Neural_InterfacesFinal published version, 638 KB

Cite this

Yassin, H., Akhoundi, A., Hasaneen, E. S., & Muratore, D. G. (2023). A Low-Power Oscillatory Feature Extraction Unit for Implantable Neural Interfaces. In ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2023-May). IEEE. https://doi.org/10.1109/ISCAS46773.2023.10181914

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title = "A Low-Power Oscillatory Feature Extraction Unit for Implantable Neural Interfaces",

abstract = "Power and area efficient on-chip feature extraction is needed for future closed-loop neural interfaces. This paper presents a feature extraction unit for neural oscillatory synchrony that bypasses the phase extraction step to reduce hardware complexity. Instead, the sine and cosine of the phase are directly approximated from the real and imaginary components of the signal to calculate the phase-amplitude coupling (PAC) and phase locking value (PLV). The synthesized design achieves state-of-the-art performances at 43 nW/channel and 0.006 mm2, while maintaining sufficient accuracy for seizure detection in epileptic patients.",

author = "Hoda Yassin and Arash Akhoundi and Hasaneen, {El Sayed} and Muratore, {Dante G.}",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 ; Conference date: 21-05-2023 Through 25-05-2023",

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doi = "10.1109/ISCAS46773.2023.10181914",

language = "English",

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Yassin, H , Akhoundi, A, Hasaneen, ES & Muratore, DG 2023, A Low-Power Oscillatory Feature Extraction Unit for Implantable Neural Interfaces. in ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings. Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2023-May, IEEE, 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023, Monterey, United States, 21/05/23. https://doi.org/10.1109/ISCAS46773.2023.10181914

A Low-Power Oscillatory Feature Extraction Unit for Implantable Neural Interfaces. / Yassin, Hoda ; Akhoundi, Arash; Hasaneen, El Sayed et al.
ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings. IEEE, 2023. (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2023-May).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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AB - Power and area efficient on-chip feature extraction is needed for future closed-loop neural interfaces. This paper presents a feature extraction unit for neural oscillatory synchrony that bypasses the phase extraction step to reduce hardware complexity. Instead, the sine and cosine of the phase are directly approximated from the real and imaginary components of the signal to calculate the phase-amplitude coupling (PAC) and phase locking value (PLV). The synthesized design achieves state-of-the-art performances at 43 nW/channel and 0.006 mm2, while maintaining sufficient accuracy for seizure detection in epileptic patients.

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A Low-Power Oscillatory Feature Extraction Unit for Implantable Neural Interfaces

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