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Discriminative Methods for Classification of Asynchronous Imaginary Motor Tasks From EEG Data
Authors: Jaime Fernando Delgado Saa, Müjdat Çetin
Published in: IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 5, pp. 716-724, September 2013
Publication year: 2013
Abstract: In this work, two methods based on statistical models
that take into account the temporal changes in the electroen-cephalographic (EEG) signal are proposed for asynchronous
brain–computer interfaces (BCI) based on imaginary motor tasks.
Unlike the current approaches to asynchronous BCI systems that
make use of windowed versions of the EEG data combined with
static classifiers, the methods proposed here arebasedondiscrim-inative models that allow sequential labeling of data. In particular,
the two methods we propose for asynchronous BCI are based
on conditional randomfields (CRFs) and latent dynamic CRFs
(LDCRFs), respectively. We describe how the asynchronous BCI
problem can be posed as a classification problem based on CRFs
or LDCRFs, by defining appropriate random variables and their
relationships. CRF allows modeling the extrinsic dynamics of data,
making it possible to model the transitions between classes, which
in this context correspond to distinct tasks in an asynchronous BCI
system. On the other hand, LDCRF goes beyond this approach by
incorporating latent variables that permit modeling the intrinsic
structure for each class and at the same time allows modeling
extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set
recorded in our laboratory.Results obtained are compared to the
top algorithm in the BCI competition as well as to methods based
on hierarchical hidden Markov models (HHMMs), hierarchical
hidden CRF (HHCRF), neural networks based on particle swarm
optimization (IPSONN) and to a recently proposed approach
based on neural networks and fuzzy theory, the S-dFasArt. Our
experimental analysis demonstrates the improvements provided
by our proposed methods in terms of classification accuracy.
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