Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

Xiaying Wang; Michele Magno; Lukas Cavigelli; Mufti Mahmud; Claudia Cecchetto; Stefano Vassanelli; Luca Benini

doi:10.1109/BIOCAS.2018.8584830

Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

Xiaying Wang^*, Michele Magno, Lukas Cavigelli, Mufti Mahmud, Claudia Cecchetto, Stefano Vassanelli, Luca Benini

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16×16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8% test accuracy in predicting the whisker deflection. The trained ML model is successfully implemented on a low-power embedded system with an average consumption of 2.6 mW.

Original language	English
Title of host publication	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538636039
DOIs	https://doi.org/10.1109/BIOCAS.2018.8584830
State	Published - 20 Dec 2018
Externally published	Yes

Publication series

Name	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Bio-sensors
Brain-chip Interface
Image Processing
Implantable Sensors
Machine Learning
Neuroscience

ASJC Scopus subject areas

Electrical and Electronic Engineering
Health Informatics
Instrumentation
Signal Processing
Biomedical Engineering

Access to Document

10.1109/BIOCAS.2018.8584830

Cite this

Wang, X., Magno, M., Cavigelli, L., Mahmud, M., Cecchetto, C., Vassanelli, S., & Benini, L. (2018). Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array. In 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings Article 8584830 (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2018.8584830

Wang, Xiaying ; Magno, Michele ; Cavigelli, Lukas et al. / Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array. 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings).

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title = "Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array",

abstract = "This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16×16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8\% test accuracy in predicting the whisker deflection. The trained ML model is successfully implemented on a low-power embedded system with an average consumption of 2.6 mW.",

keywords = "Bio-sensors, Brain-chip Interface, Image Processing, Implantable Sensors, Machine Learning, Neuroscience",

author = "Xiaying Wang and Michele Magno and Lukas Cavigelli and Mufti Mahmud and Claudia Cecchetto and Stefano Vassanelli and Luca Benini",

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year = "2018",

month = dec,

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doi = "10.1109/BIOCAS.2018.8584830",

language = "English",

series = "2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings",

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Wang, X, Magno, M, Cavigelli, L, Mahmud, M, Cecchetto, C, Vassanelli, S & Benini, L 2018, Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array. in 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings., 8584830, 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/BIOCAS.2018.8584830

Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array. / Wang, Xiaying; Magno, Michele; Cavigelli, Lukas et al.
2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8584830 (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Cecchetto, Claudia

AU - Vassanelli, Stefano

AU - Benini, Luca

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AB - This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16×16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8% test accuracy in predicting the whisker deflection. The trained ML model is successfully implemented on a low-power embedded system with an average consumption of 2.6 mW.

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KW - Brain-chip Interface

KW - Image Processing

KW - Implantable Sensors

KW - Machine Learning

KW - Neuroscience

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ER -

Wang X, Magno M, Cavigelli L, Mahmud M, Cecchetto C, Vassanelli S et al. Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array. In 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8584830. (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings). doi: 10.1109/BIOCAS.2018.8584830

Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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