Title: Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees,
Abstract: Objective: Using traditional approaches, a Brain-Computer Interface (BCI)
requires the collection of calibration data for new subjects prior to online
use. Calibration time can be reduced or eliminated e.g.~by transfer of a
pre-trained classifier or unsupervised adaptive classification methods which
learn from scratch and adapt over time. While such heuristics work well in
practice, none of them can provide theoretical guarantees. Our objective is to
modify an event-related potential (ERP) paradigm to work in unison with the
machine learning decoder to achieve a reliable calibration-less decoding with a
guarantee to recover the true class means.
Method: We introduce learning from label proportions (LLP) to the BCI
community as a new unsupervised, and easy-to-implement classification approach
for ERP-based BCIs. The LLP estimates the mean target and non-target responses
based on known proportions of these two classes in different groups of the
data. We modified a visual ERP speller to meet the requirements of the LLP. For
evaluation, we ran simulations on artificially created data sets and conducted
an online BCI study with N=13 subjects performing a copy-spelling task.
Results: Theoretical considerations show that LLP is guaranteed to minimize
the loss function similarly to a corresponding supervised classifier. It
performed well in simulations and in the online application, where 84.5% of
characters were spelled correctly on average without prior calibration.
Significance: The continuously adapting LLP classifier is the first
unsupervised decoder for ERP BCIs guaranteed to find the true class means. This
makes it an ideal solution to avoid a tedious calibration and to tackle
non-stationarities in the data. Additionally, LLP works on complementary
principles compared to existing unsupervised methods, allowing for their
further enhancement when combined with LLP.