Rat cortical layers classification extracting evoked local field potential images with implanted multi-electrode sensor
Abstract
One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data processing is needed to extract useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, machine learning approaches are increasingly used in many application scenarios. This paper focuses on processing data of evoked local field potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16 16 MEA. We evaluated machine learning algorithms and trained an optimized classifier to detect at which cortical depth the neural activity is measured. We demonstrate with experimental results that machine learning can be applied successfully to noisy single-trial LFPs offering up to 99.11% of test accuracy in classifying signals acquired from different cortical layers. As such, the method is a very promising starting point toward realtime decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000283870Publication status
publishedExternal links
Book title
2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)Pages / Article No.
Publisher
IEEEEvent
Subject
neuroscience; machine learning; brain-chip interface; image processing; bio-sensors; implantable sensorsOrganisational unit
03996 - Benini, Luca / Benini, Luca
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