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BCI-controlled Drone

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Biowolf drone.jpg

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.

In this project, the student works on the acquisition and processing of electroencephalography (EEG) data to control a drone using a Brain-Machine Interface (BMI) with motor imagery (MI). This includes:

  • Set up experimental paradigm to acquire EEG data in the motor imagery paradigm
  • Record data with of multiple subjects (potentially from our lab)
  • Analyse the data and apply a standard classifier [1,2]
  • Move forward to online asynchronous BCI experiments
  • Potentially control a drone


Status: Available

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

Prerequisites

  • Machine Learning
  • Python and C Programming
  • Embedded Systems


Character

15% Theory
45% Implementation
40% Testing

Professor

Luca Benini

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Literature

  • [1] M. Hersche, et al., Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features, 2018
  • [2] X. Wang, et. al., An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing, 2020

Practical Details

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