Systems, Man & Cybernetics - October 2016 - 5

kinematics research in BCI, which uses neural activity to
decode movement parameters such as speed, direction,
position, and force. Furthermore, just as the neural activation patterns differ for various motor tasks and limbs, specific localized areas of the limbs (arm, elbow, shoulder,
hip, and knee) also generate discriminative activity. Identifying such cortical activations underlying these tasks from
scalp-recorded brain activity can have a huge impact on
neuromotor rehabilitation applications.
The objective of these areas of SMR-BCI research is to
provide high-precision, continuous, and accurate motor
control to the interfaced device. The existing BCI research
sets performance goals, including high information transfer
rate, low decoding error, high classification accuracy,
robustness, portability, and cost efficiency, while designing
a system. To attain each of these goals, the major challenge
is to identify the neural phenomenon underlying finer
movement tasks, using scalp-recorded brain signals. The
development of signal processing and machine learning
algorithms [98], [106], [107] over the
years has enabled BCI to work toward
these goals. The various brain data
acquisition modalities record neuronal
activations appearing at different cortical levels or on the scalp surface,
which result in different signal spatial
resolution. Specifically, for noninvasive
techniques that record extracellular
potentials over the scalp, the signal has
limited spatial resolution and frequency range, and the technique is highly
susceptible to environment interference and muscular or ocular artifacts.
As a result, these factors are major
challenges in noninvasive BCI re search. Recent studies have established the use of noninvasive signals
for movement kinematics and finer
motor control, despite the assumption
that these parameters are encoded in
the neuronal firing [4]-[7].
Related Work

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Background
The primary focus area of BCI research is the neurological
disorders that affect the motor cortex of the brain, leaving
the patients with severe motor disabilities and loss of manual dexterity [1]. This has resulted in the development of
motor-control BCIs that function depending on the SMRs
of the brain [1]-[3]. Human motor abilities and their underlying phenomena are explored using invasive and noninvasively recorded brain activity. SMR-based BCIs focus on
characterizing and differentiating the neural features
responsible for various motor tasks. The translation of
neural activity corresponding to bilateral limb movement
execution or imagination is a widely popular motor BCI
control output. As indicated in Figure 1, in a BCI system,
recorded neural data undergo signal processing and
machine learning algorithms that identify neural features
that can be translated to commands to control external
devices. The demand of continuous and control output
with higher degrees of freedom introduced movement

O c tob e r 2016

BCI for Communication
and Control
BCI technology has been introduced
with the goal of providing augmentative communication and control for
those with severe neuromuscular disorders [1], [7], [8]. However, researchers
have also investigated the nonmedical
applications of BCI [95], thus developing brain-controlled devices aimed at
performance en hancement or entertainment. In this section, an overview
IEEE SyStEmS, man, & CybErnEtICS magazInE

5


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Table of Contents for the Digital Edition of Systems, Man & Cybernetics - October 2016

Systems, Man & Cybernetics - October 2016 - Cover1
Systems, Man & Cybernetics - October 2016 - Cover2
Systems, Man & Cybernetics - October 2016 - 1
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Systems, Man & Cybernetics - October 2016 - Cover3
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