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Predict eye movement through brain activity

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Revision as of 11:05, 17 January 2023 by Thoriri (talk | contribs)
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Introduction and Project Description

Nowadays, many people use fitness wearables to monitor health conditions. They can track calorie intake, step number, sleep quality, and heart rate. What hasn't been fully explored yet are wearables with sensors to track brain activity. Recorded brain activity could provide relevant information for attention, decision-making, health purposes, or even eye movements. One of the possible approaches to this problem is a measurement of brain activity with sensors in glasses or a headband. The advertised project aims to provide a proof of concept of this solution. We plan to measure EEG/EOG activity with dry electrodes and relate them to eye movement.

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Your task:

  • Design and conduct an experiment where you measure EEG/EOG and eye movement. We will provide you with the material including the EEG/EOG wearable and an eye tracker setup.
  • Analyze the robustness/limitations of your experimental setup.
  • Use Machine Learning methods to predict eye movement through EEG/EOG data. This will build on former work (

Status: Available

Looking for Master Project Students
Supervision: Thorir Mar Ingolfsson, Andrea Cossettini, Simone Benatti, Martyna Plomecka, Ard Kastrati, Anh Duong

Your Profile

  • Background in Engineering (Biomedical, Mechanical, Chemical…)
  • Familiar with Deep Learning, experience with TensorFlow or PyTorch
  • Motivation to work on a project at the intersection of Neuroscience and Engineering
  • Willingness to work in an interdisciplinary team (Hardware, Machine Learning, Psychology, Neuroscience)

Reach out

If you are interested, we would love to get to know you! Please write an Email to all of us, including your motivation, CV, and transcript of records:

  • Thorir Mar Ingolfsson:
  • Martyna Plomecka,
  • Ard Kastrati,
  • Anh Duong Vo,


Luca Benini

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