IEEE Computational Intelligence Magazine - May 2018 - 66
Research
Society
Society
Frontier
Briefs
David Hübner
Brain State Decoding Lab, University of Freiburg, Freiburg, GERMANY
Thibault Verhoeven
Electronics and Information Systems, Ghent University, Ghent, BELGIUM
Klaus-Robert Müller
Machine Learning Group, Berlin Institute of Technology, Berlin, GERMANY
Department of Brain and Cognitive Engineering, Korea University,
Seoul, KOREA
Max Planck Institute for Informatics, Saarbrücken, GERMANY
Pieter-Jan Kindermans
Machine Learning Group, Berlin Institute of Technology, Berlin, GERMANY
Michael Tangermann
Brain State Decoding Lab, University of Freiburg, Freiburg, GERMANY
Unsupervised Learning for Brain-Computer Interfaces Based on
Event-Related Potentials: Review and Online Comparison
Abstract
ne of the fundamental challenges
in brain-computer interfaces
(BCIs) is to tune a brain signal
decoder to reliably detect a user's intention. While information about the
decoder can partially be transferred
between subjects or sessions, optimal
decoding performance can only be
reached with novel data from the current
session.Thus, it is preferable to learn from
unlabeled data gained from the actual
usage of the BCI application instead of
conducting a calibration recording prior
to BCI usage. We review such unsupervised machine learning methods for BCIs
based on event-related potentials of the
electroencephalogram. We present results
of an online study with twelve healthy
participants controlling a visual speller.
Online performance is reported for three
completely unsupervised learning methods: (1) learning from label proportions,
(2) an expectation-maximization approach
and (3) MIX, which combines the
strengths of the two other methods. After
a short ramp-up, we observed that the
MIX method not only defeats its two
o
unsupervised competitors but even perphase prior to the actual usage of the
forms on par with a state-of-the-art regapplication. During calibration, the user is
ularized linear discriminant analysis
requested to perform a series of pretrained on the same number of data
defined tasks in order to collect example
points and with full label access. With this
data, for which the user's intentions are
online study, we deliver the best possible
known. Machine Learning (ML) methods
proof in BCI that an unsupervised
then use this labeled data to learn the
decoding method can in pracsubject-specific brain signal
tice render a supervised
characteristics and predict
method unnecessary.
the user's intention on
This is possible denew unseen data. In
spite skipping the
the field of Braincalibration, without
Computer Interlosing much perfaces (BCI), these
formance and with
predictions can
the prospect of
enable the user to
con tinuous im control applicaprovement over a
tions and physical
session. Thus, our
devices by translating
findings pave the way
brain activity into con©is
for a transition from suptrol
commands [1]-[5].
0
toc
500
kpho
rik
to.com/artist hen
ervised to unsupervised learnOne research goal in BCIs is
ing methods in BCIs based on eventto replace lost or faulty neurobiological
related potentials.
pathways, e.g., for patients with Amyotrophic Lateral Sclerosis (ALS) [6]-[8], by
computational intelligence, for instance, to
I. Introduction
restore communication [2], [9] or control
Many applications in the field of humana wheelchair [10].
device interaction need a calibration
Digital Object Identifier 10.1109/MCI.2018.2807039
Date of publication: 10 April 2018
Corresponding authors: Pieter-Jan Kindermans (E-mail: p.kindermans@tu-berlin.de) and Michael Tangermann
(E-mail: michael.tangermann@blbt.uni-freiburg.de)
66
IEEE ComputatIonal IntEllIgEnCE magazInE | may 2018
1556-603x/18©2018IEEE
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