Systems, Man & Cybernetics - October 2016 - 11
Thumb
-6
0
Index
6
10-14
-6
Middle
0
6
-5
0
10-14
Averaged Evoked Fields in T
Little
5
10-14
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Figure 5. the evoked magnetic fields are shown for movements of the thumb, index, middle, and little finger of
the right hand [76].
x-Coordinate
x-Coordinate
in Table 3, to improve the classification performance of
addresses the challenges of overlapping cortical sources.
these multiclass BCI systems. Also, studies such as [69] and
The investigations in this area focus on movement types
[75] have reported single-trial classifications of elbow/shoulexecuted by different areas of the same limb. Studies have
der/finger movements in disabled subjects as well. The BCI
investigated upper limb movement (hand grasping, pinchresearch on lower limb movements was reported in [92],
ing, and elbow flexion) using MEG [65], finger movement
using fMRI to identify cortical source of bilateral foot movetasks using functional magnetic resonance imaging
ment imagery and in [93] and [94] using EEG to perform sin(fMRI) [66], and combined MEG-EEG [67]. Figure 6 shows
gle-trial classifications of foot movement. The research
the relatively similar activation patterns in terms of averindicates the feasibility of EEG signals to decode localized
age evoked magnetic field recorded by MEG for different
limb movements. A less-explored topic in this BCI research
finger movements.
seems to be investigation of spatially localized regions
A summary of various EEG studies mentioned in this
responsible for distinct limb areas using EEG. Although the
section is given in Table 3:
spatial resolution of EEG makes this a challenging task, the
◆ classification of imagined fast and slow wrist extensignificant findings in the literature makes this a promising
sion and rotation, elbow versus shoulder torque
area for future research.
intention [68]
◆ imagined movement involving three limbs (left and
SMR-BCI Applications
right index finger and right toe) [69]
The ultimate objective of all the studies mentioned in
◆ imagined movement of finger and wrist [70]
the previous sections is to impart higher-dimensional
◆ imagined wrist flexion and extension of right and left
wrists [71]
◆ self-initiated movement tasks (shoulder abduction, extension and rotation,
Speed
Position
elbow extension, forearm pronation,
wrist extension, and rotation) [72]
0.2
0.4
◆ imaginary grasp movements and imaginary elbow movements [73]
0.1
0.2
◆ five finger movements of the same
0
0
hand [74], [75]
-0.1
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◆ nine index finger position during a
key-pressing task [76]
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-0.2
-0.2
-0.1
◆ real and imagined movements using
0
0
140
150
0.2
index and thumb fingers [77].
0.1
100120
100
0.4
80
60
y-Coordinate
y-Coordinate
50
The neural features, such as mu/beta
Samples
Samples
band powers, movement-related cortical
Recorded Parameter
Estimated Parameter
potential (MRCP), event-related potentials,
and ERD/ERS patterns, are of interest in
these BCIs. A wide range of algorithms Figure 6. the 2-D hand-movement trajectory parameters reconstructed
were employed in these studies, as shown using Kalman-filter-based adaptive estimator with selected predictors [98].
O c tob e r 2016
IEEE SyStEmS, man, & CybErnEtICS magazInE
11
Table of Contents for the Digital Edition of Systems, Man & Cybernetics - October 2016
Systems, Man & Cybernetics - October 2016 - Cover1
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Systems, Man & Cybernetics - October 2016 - 1
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Systems, Man & Cybernetics - October 2016 - Cover3
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