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OPEN 8 ACCESS Freely available online
: PLOS I ONE
Collaborative Filtering for Brain-Computer Interaction
Using Transfer Learning and Active Class Selection
Dongrui Wu'*, Brent J. Lance2, Thomas D. Parsons3
1 Machine Learning Laboratory, GE Global Research, Niskayuna, New York, United States of America, 2 Army Research Laboratory, Aberdeen Proving Ground, Aberdeen,
Maryland, United States of America, 3 Department of Psychology, University of North Texas, Denton, Texas, United States of America
Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or
physiological signals to influence human interaction with computers, environment, and each other. A major challenge in
developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As
a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time,
cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to
increase the recognition accuracy without increasing the number of user-specific training samples. One promising method
for achieving this is collaborative filtering, which combines training data from the individual subject with additional training
data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended
for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference
user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty
recognition. TL improves the learning performance by combining a small number of user-specific training samples with a
large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-
specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine
classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data
samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other
applications that involve human neural or physiological data, such as affective computing.
Citation: Wu D, Lance BJ, Parsons TD (2013) Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection. PLoS
ONE 8(2): e56624. doi:10.1371/journal.pone.0056624
Editor: Derek Abbott, University of Adelaide, Australia
Received October 24, 2012; Accepted January 15, 2013; Published February 21, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CCO public domain dedication.
Funding: The authors have no support or funding to report.
Competing Interests: The first author is with a commercial company (GE Global Research). This does not alter the authors' adherence to all the PLOS ONE
policies on sharing data and materials.
* E-mail: email@example.com
Future technologies that allow computer systems to adapt to
individual users - or even to the current cognitive/affective state of
the user - have many potential applications including entertain-
ment, training, communication, and medicine. One promising
avenue for developing these technologies is through brain-
computer interaction (BCI) or physiological computing; i.e, using
processed neural or physiological signals to influence human
interaction with computers, environment, and each other [1,2].
There are numerous challenges to effectively using these signals in
system development. One of the primary challenges is the
individual differences in neural or physiological response to tasks
or stimuli. In order to address these individual differences, many
researchers train or calibrate their systems for each individual,
using data collected from that individual. However, the time spent
collecting this data is likely to decrease the utility of these systems,
slowing their rate of acceptance. As an example, one of the
primary reasons that slow cortical potential-based BCIs never
achieved mainstream acceptance, even among the disabled, is
because using the slow cortical potential-based BCI could require
training for several hour-long sessions per week for months in
order to achieve satisfactory user performance .
While it is possible to train a generic model with group or
normative data, in practice this tends to result in significantly
lower performance than calibrating with individual data . An
example of this may be found in our earlier work , in which we
used a support vector machine (SVM) to classify three task
difficulty levels from neural and physiological signals while a user
was immersed in a virtual reality based Stroop task , which has
been shown to have high individual differences in neural and
physiological response as the task difficulty varies . Results
revealed that when each subject is considered separately, an
average classification rate of 96.5% can be obtained by SVM;
however, the average classification rate was much lower (36.9%,
close to chance) when a subject's perception of task difficulty level
was predicted using only data from other subjects. In a more
recent study  on whether generic model works for rapid event-
related potential (ERP)-based BCI calibration, a generic model
was derived from 10 participants' data and tested on the 1 th
participant. Experiments showed that seven of the 11 participants
were able to use the generic model during online training, but the
remaining four could not.
Novel approaches to analyses of individual differences have
significant potential in helping to address these individual
differences in neural and physiological responses [9,10]. In
February 2013 1 Volume 8 1 Issue 2 1 e56624
PLOS ONE I www.plosone.org
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Wu, Dongrui; Lance, Brent J. & Parsons, Thomas D. Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection, article, February 21, 2013; [San Francisco, California]. (digital.library.unt.edu/ark:/67531/metadc306986/m1/1/: accessed November 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.