Systems, Man & Cybernetics - October 2016 - 10

Table 2. Hand movement kinematics decoding.
Result (Correlation
Coefficient, Remarks)

Modality

Study

Movement Parameter/Task

Feature/Technique

EEG

[54], [55]

3-D center-out hand movement

Linear decoding model

(0.19, 0.38, 0.32) (x, y, z) axes
of speed

[56]

2-D drawing task

Kalman smoother decoder

0.35-0.83 (horizontal) and
0.11-0.45 (vertical)

[58]

3-D virtual space target-reaching
intention

Stepwise regression

(0.2-0.39, 0.09-0.5, 0.09-0.57) for
vertical, horizontal, depth

[59]

Natural grasping: finger
trajectories

Linear decoding model

0.76

[60]

Eight-target center-out hand
movement

Linear multiple regression model

0.76

[61]

Self-paced and self-initiated
movement

Partial least squares and support
vector regression

0.2-0.4

[62]

3-D center-out movement to
self-chosen targets

Linear decoding model

0.7, 0.77, 0.62) and (0.7, 0.78,
0.62) for x, y, and z coordinates
of velocity and position

[63]

Movement kinematics: filling a
glass of water

Filter bank CSP and regression
models

(0.41, 0.36, 0.48, 0.17) for x, y, z
and absolute values of speed

[43], [64], [65]

2-D center-out four-target
reaching task at two different
speeds

Wavelet CSP, Kalman filters

0.57 (average over six
parameters), 76% reduction
in number of predictors.

[66]

2-D target selection task

Linear decoding models

(0.39, 0.47) for (x, y) axes of
velocity

[67]

Velocity of hand/elbow during
a center-out movement task

Linear decoding models

(0.31, 0.27, 0.15) and (0.31, 0.3,
0.16) for (x, y, z) axes of hand
and elbow velocity

[70]

2-D and 3-D hand movement
position

Particle filter model

Higher decoding accuracy
compared to linear decoders

[71]

3-D movement velocities

Kernel ridge regression

Significant reduction in
decoding error

[72]

3-D reach to grasp task

Independent component
analysis, Wiener filter, multiple
kernel learning

(0.59, 0.47, 0.32) for first three
kinematic synergies

[73]

2-D finger movement

Alpha and beta band powers

Coefficient of determination:
0.21-0.31

5.2%

6.9%

16.0%

9.1%

10.1%

5.0%

z = 42

Sensor Contribution

z = 47
z = 52
5.6%

-100

-90

10.7%

-80

-70

15.6%

-60

-50
-40
Time (ms)
(a)

8.5%

-30

7.3%

-20

-10

0
(b)

Figure 4. (a) the scalp maps indicating a contribution toward decoding hand-movement velocity. (b) the
localized EEg sources from −60 ms overlaid onto magnetic resonance imaging structural images [55].
10

IEEE SyStEmS, man, & CybErnEtICS magazInE October 2016



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