IEEE Systems, Man and Cybernetics Magazine - July 2020 - 12

SPECIAL SECTION
B R A I N - M AC H I N E I N T E R FAC E S Y S T E M S

Brain-
Machine
Interfaces
©ISTOCKPHOTO.COM/OOKDESIGN

A Tale of Two Learners
by Serafeim Perdikis and José del R. Millán

B

rain-machine interface (BMI) technology has
rapidly matured over the last two decades,
mainly thanks to the introduction of artificial
intelligence (AI) methods, in particular,
machine-learning algorithms. Yet, the need
for subjects to learn to modulate their brain activity is a
key component of successful BMI control. Blending
machine and subject learning, or mutual learning, is widely acknowledged in the BMI field. Nevertheless, we posit
that current research trends are heavily biased toward the
machine-learning side of BMI training. In this article, we
take a critical view of the relevant literature, and our own
previous work, to identify the key issues for more effective
mutual-learning schemes in translational BMIs that are
specifically tailored to promote subject learning. We identify the main caveats in the literature on subject learning
in BMI, in particular, the lack of longitudinal studies
involving end users and shortcomings in quantifying subject learning, and pinpoint critical improvements for future
experimental designs.

Digital Object Identifier 10.1109/MSMC.2019.2958200
Date of current version: 15 July 2020

12

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Ju ly 2020

The Need for Mutual Learning
BMI technology has rapidly matured over the last two
decades resulting in the first clinical evaluations of assistive or rehabilitation prototypes for people in paralysis [1].
Early BMI approaches mainly relied on the brain's ability to
gradually learn to regulate its activity through feedback
provision, a technique that borrows from neurofeedback
research and applies operant conditioning principles [2]-
[4]. This subject-learning process is believed to be fueled by
functional and structural plastic transformations within
the brain itself [5]-[8], which is known to often require
lengthy training. A breakthrough in BMI took place when
AI algorithms were introduced, enabling instead the
machine to learn-in much shorter time scales-to decode
the (high-dimensional) multivariate and noisy brain activity patterns of various mental tasks [9]. This state-of-the-art
machine-learning approach is based on the premise that
different "thoughts" or "actions" must have unique, preexisting neural substrates, which, if adequately captured by
brain imaging technology, should be distinguishable thanks
to the power of modern AI algorithms. Reconciling these
competing practices, the opinion that future BMIs should
consider, facilitate and coordinate both learning agents
2333-942X/20©2020IEEE


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IEEE Systems, Man and Cybernetics Magazine - July 2020

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