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Towards global Brain-Computer Interfaces

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Description

A brain–computer interface (BCI) is a device that enables communication between the human brain and an external device. It aims to recognize the human’s intentions from spatiotemporal neural activity typically recorded by a large set of electroencephalogram (EEG) electrodes. What makes it particularly challenging, however, is its susceptibility to errors in the recognition of human intentions, especially during motor imagery (MI). The underlying reason is the high inter-subject variance, which makes it difficult to build one universal model for all subjects.

This project aims to find a global model using hyperdimensional superposition of model weights [1]. A preliminary study showed that we can find a global model for 9 subjects on a 4-class MI dataset. In this project, you apply hyperdimensional superposition on a much larger dataset with 109 subjects. Furthermore, you will apply the same methodology on different BCI paradigms at the same time, e.g. P300 and MI, aiming for a model able to process different brain signals simultaneously.


Status: Available

Looking for 1-2 students for a semester project or group project.
Supervision: Michael Hersche Abbas Rahimi

Prerequisites

  • Machine Learning
  • Python Programming


Character

20% Theory
80% Programming

Professor

Luca Benini

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Literature

  • [1] Cheung et al., Superposition of many models into one
  • [2] Schirrmeister et. al., Deep learning with convolutional neural networks for EEG decoding and visualization

Practical Details

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