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Real-Time Embedded Classification of Neural Activity in Rat Barrel Cortex

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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. Neural coding is the branch of neuroscience which investigates the relationship between stimulus and neuronal responses. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multielectrode-arrays (MEAs) to record brain signals at high spatio-temporal resolution. Data processing is needed to decode 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, artificial intelligence combined with low-power embedded devices is a very promising starting point towards real-time decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications.


This project focuses on processing data of evoked Local Field Potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16-by-16 MEA while stimulating the principal whisker. The sensor has been implanted in vivo and 2D images have been acquired from different cortical depths. The deflection of the whisker is performed by means of a piezo-electric bender using various stimulation amplitudes. The aim of the project is to extract and evaluate relevant features and to identify the best machine learning approach to detect the signal onset and to infer the type of the stimulation sensed by the rat. Furthermore, the selected algorithms for feature extraction and the classifier are meant to be implemented on a low-power embedded system. Given the constrained conditions under which we operate, i.e. implantable devices, energy-efficiency is of paramount importance.

The task includes the following main sub-points:

  • Understand the LFP basics and interpret the dataset.
  • Develop (high-level Phython or Matlab) machine learning or deep learning algorithm to classify the stimulation amplitudes or to detect signal onset.
  • Map the algorithm in the hardware (C-programming PULP, parallel computing).
  • Conduct in-vivo experiments to validate the method with a realistic setting.

The task is anyways flexible and it will be adapted to the student's skills and will.

Status: Completed

  • Semester project in Fall 2019 (Nami Hekmat, sem19h22)
Supervisors: Xiaying Wang, Michele Magno, Lukas Cavigelli


35% Theory and Algorithms
45% Implementation
20% Verification and Testing


Knowledge in Machine Learning (preprocessing, feature extraction, classifier, supervised-learning)
Embedded system programming
Basic analog / ADC / sampling theory hands-on knowledge.
Matlab, Python, C/C++

IIS Professor

Luca Benini

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Practical Details