Physics-informed and Unsupervised Riemannian Domain Adaptation for
  Machine Learning on Heterogeneous EEG Datasets
Apolline Mellot Antoine Collas Sylvain Chevallier Denis Engemann Alexandre Gramfort
Combining electroencephalogram (EEG) datasets for supervised machine learning
(ML) is challenging due to session, subject, and device variability. ML
algorithms typically require identical features at train and test time,
complicating analysis due to varying sensor numbers and positions across
datasets. Simple channel selection discards valuable data, leading to poorer
performance, especially with datasets sharing few channels. To address this, we
propose an unsupervised approach leveraging EEG signal physics. We map EEG
channels to fixed positions using field interpolation, facilitating source-free
domain adaptation. Leveraging Riemannian geometry classification pipelines and
transfer learning steps, our method demonstrates robust performance in
brain-computer interface (BCI) tasks and potential biomarker applications.
Comparative analysis against a statistical-based approach known as
Dimensionality Transcending, a signal-based imputation called ComImp,
source-dependent methods, as well as common channel selection and spherical
spline interpolation, was conducted with leave-one-dataset-out validation on
six public BCI datasets for a right-hand/left-hand classification task.
Numerical experiments show that in the presence of few shared channels in train
and test, the field interpolation consistently outperforms other methods,
demonstrating enhanced classification performance across all datasets. When
more channels are shared, field interpolation was found to be competitive with
other methods and faster to compute than source-dependent methods.