Systems, Man & Cybernetics - October 2016 - 7

asynchronous BCI systems. BCI research on discriminating limb areas, such as wrist/elbow/shoulder and knee/hip,
is relevant since stroke-related impairments affecting
movement coordination can be better studied using this
approach rather than gross movement activation patterns.
Various brain studies have reported the cortical reorganization following stroke, resulting in overlap of cortical areas
corresponding to independent joints [15], [16].
Neural Encoding of Movement Kinematics
The investigation of neuronal activation patterns responsible for hand-movement kinematics have been performed
using invasive brain recording of primates as well as
humans. The goal of these works have been to shed light
on the neural encoding and connectivity that enables one
to perform coordinated and defined motor tasks. In [6], the
authors reviewed the directional tuning of neural signals
and the literature on decoding movement direction and
trajectory using multiple BCI modalities. Furthermore, the
review reported in [5] provided a comprehensive overview
of invasive and noninvasive studies in movement kinematics decoding in humans and in primates.
Various studies have demonstrated directional tuning of
neuronal parameters, i.e., its variation with the direction of
movement. Center-out hand-movement experiments were
used in these studies. Single-unit activity and multiunit
activity studies in primates showed that the movement
direction was found to depend on the neuronal firing rate
[17]. More recent studies reported the ability of an ensemble
of neuronal activity to control a robotic arm to perform
reach/grasp movement [18] and that of localized field potentials to decode movement trajectories and velocity [19]. Further neural data recorded from cortical surface using
electrocorticography also provided local motor potential
features that demonstrated direction tuning and twodimensional (2-D) four-target movement decoding [19], [20].
The next sections discuss the state-of-the-art techniques in
finer motor control using noninvasive BCIs.
Discrete Classification
of Movement Parameters
In implementing a BCI with higher degrees of freedom, the
identification of the neural correlates of motor kinematics
is of prime importance. The noninvasive research in this
area aimed to identify and extract the neural features that
are responsible for precise motor control. In this section,
we list the recent findings in BCI research that used noninvasive brain data acquisition modalities to discretely classify hand-movement parameters such as direction, arm
force, and speed. Some special cases are also mentioned
that investigated speed/force imagery, rhythmic imagined
movement, and expressive movements.
The tasks adopted by the researchers include centerout and center-in movements that are either visually guided or self-paced. Various studies have even attempted
natural movements such as drawing, clenching, and

reaching. All this research points toward specific spectral
and spatial distribution of neural activity associated with
finer movement. The involvement of the motor and parietal
cortex has been repeatedly proven in all the studies as
well as the neural features from low-frequency (<8 Hz)
bands. A wide range of algorithms were reported that use
time-frequency (TF)-space localized features to classify
the movement parameters. According to the results reported in the literature, spectrally localized neural data and optimized algorithms based on a common spatial pattern
(CSP) provide better classification performance and
hence are widely explored. The results obtained from
these studies are summarized in Table 1, and we will discuss the details.
The foremost study on identifying hand-movement
parameter direction using a noninvasive brain signal was
reported in [21]. The movement direction was classified
on a single-trial basis using magnetoencephalography
(MEG) signal power in low-frequency bands. The study
simultaneously recorded electroencephalography (EEG)
and 3-Hz low-pass-filtered EEG and MEG features were
analyzed. Reported in [22] and [23] was that the cortical
sources of movement direction were using source-localization methods. Functional near infrared spectroscopy
(fNIRS) has been used by various researchers to investigate arm movement force. The hemoglobin concentration
changes as the subject performed isometric arm movement were used to discriminate the force direction, and
results of classification were reported in [24] and [25]. The
direction tuning of fNIRS-based hemodynamic signals
recorded over contralateral sensorimotor areas were
demonstrated in [26].
The role of posterior parietal cortex in encoding handmovement direction and intended movement direction
was studied in [27] and [28]. The studies [29]-[31] investigated the TF regions that contain optimal movement
directional information that can enhance the decoding
and classification performance of EEG-BCI systems. The
TF bins that provide higher direction-dependent information from specific electrodes were detailed in [29]-[31].
Reported in [31] was a significant (p < 0.005) movement
direction dependent modulation toward the end of movement at low frequencies (≤6 Hz) from the midline parietal
and contralateral motor areas, as shown in Figure 3. In
[87], neural activity related to bidirectional hand movement (imagined and executed) was recorded using a lowcost commercial EEG amplifier and decoded. Single-trial
classifications of center-out and center-in movements
were reported in [32] using CSP-based EEG features (eight
classes), [33] with dyadic filter bank CSP-based EEG
(<8  Hz) feature (four classes), and in [34] with canonical
variance analysis (four classes). In [35], SCP derived from
0.1-1-Hz EEG data was used to predict movement direction on healthy as well as stroke patients (using paretic
arm). In [36], movement direction was studied by recording data as the subject traced an infinity shape using his
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