Channel set invariance for neural networks

Devlopment of a neural network architecture invariant to different channel sets contained in EEG recordings

Problem

Despite the well-established standards for EEG electrode layout like the 10-20 montage, EEG recordings obtained in different labs or across studies are not straightforward to compare. There are usually important differences between the datasets, but also within them. These differences can be:

  • the use of different channel sets
  • different noise distributions, or changes of the noise structure over time
  • temporarily unusable channels (electrical connection lost, faulty wire, …)

For this reason, spatial filters can hardly be re-used between sessions, let alone datasets, which is an obstacle for transfer learning approaches in EEG decoding problems. The existing solutions to do transfer learning with heterogeneous channel sets are:

  • train a model on the common channel subset → restrictive, if a new recording has a faulty channel, as the whole model has to be re-trained without this channel
  • interpolate the missing channels → obtained full channel set may not be full rank any more
  • source reconstruction techniques → assumptions need to be made for source reconstruction, their choice is non-trivial

Objective

The objective of this project is to help in the development of a neural network architecture invariant to the set of channels used. Such an architecture would allow to realize an end-to-end learning approach across multiple datasets with heterogeneous channel sets. Ideally, this architecture would be able to:

  • handle EEG signals containing an arbitrary number of channels
  • produce results independently of the channels used
  • reach the same performances as non-channel independent architectures (like EEGNet) when the channel set is fixed / complete
  • seamlessly ignore corrupted channels, and degrade gracefully
  • cope in real time with potential noise distribution shifts

Skills required

  • Good programming skills in Python
  • Experience with the pytorch library
  • Optional: experience with deploying pipelines on GPU clusters
  • Optional: experience with EEG/BCI data
Michael Tangermann
Michael Tangermann
Associate Professor