Systems, Man & Cybernetics - October 2016 - 8

Table 1. Classification of hand-movement parameters.
Modality

Study

Movement Parameter/Task

Feature/Technique

Result (Classification
Accuracy, Remarks)

MEG
EEG

[24]

2-D center-out self-chosen target
hand movement

Regularized linear discriminant
analysis (LDA)

MEG: 67%; EEG: 55%
MEG+EEG: 60.2%

MEG

[25], [26]

2-D center-out visually guided fourtarget wrist movement

LDA and Bayesian classifier,
discriminant pattern source
localization

67% (overt) and 62.5%
(imagined), localized activations
for the spectral band of 0-7 Hz

fNIRS

[27]

Isometric arm movement force
to four targets

Self-organizing maps

87.5% (binary class)

[29]
EEG

Sparse logistic regression

>95% (binary class)

[30]

Visually guided four-target wrist
movement

Regularized LDA, nonlinear support
vector machine (SVM)

65% (left versus down movement)

[31]

Delayed saccade/reach

Independent component analysis

80.25% (right versus left direction)

[32]

Visually guided three-target
reaching task

Fisher linear discriminant (FLD)

93.91% (right versus left direction)

[35]

2-D center-out visually guided
four-target reaching

Regularized wavelet CSP

80.24% (four-class)

[36]

Eight-target center-out and
center-in movement

CSP

71% (binary)

[37]

2-D center-out visually guided
four-target reaching

Dyadic filter bank CSP

66.08% (four-class)

[38]

Four-target self-paced center-out
movement

Canonical variance analysis

85%

[39]

Four-target center-out movement

SCP

76% (healthy) and 47% (stroke)

[40]

Tracing infinity shape

CSP for six-class data

74% (binary)

[43]

Movement speed

Wavelet CSP

83.71% (binary)

[44]

Speed and force of clench motor
imagery

Alpha band power

67.65% (trained subjects) and
59.68% (nontrained subjects)

[46]

Imagined grip force

Movement-related cortical
potential

24% (SVM) and 27% (k-FLD)

[97]

Bidirectional center-out visually
guided movement

Filter bank CSP

81.3% (movement execution) and
82.4% (movement imagination)

right arm in the vertical plane. Various other tasks studied
using EEG include hand-movement speed [37], the parameters of motor imagery, such as speed and force specific
to clenching [38], and bilateral imagined grip force [39].
Movement Kinematics Decoding
The BCI research findings that specifically focus on
the reconstruction of hand-movement speed/velocity
and trajectory are reviewed in this section. The movement trajectory/coordinates as a hand/finger/elbow
performs a task are studied using various experiment
paradigms. The research has reported significant contribution to linear decoding by low-frequency EEG
(<3 Hz) from motor, premotor, and parietal areas. Various
nonlinear decoders were also proposed with the goal
of including more signal spectrum. The research in
this area using EEG is summarized in Table 2. The
8

IEEE SyStEmS, man, & CybErnEtICS magazInE October 2016

performance metric, indicated in the results, is the correlation coefficient between recorded and reconstructed movement parameters.
The ability of an MEG signal to decode hand-movement
kinematics was explored in [40]-[44]. In [40] and [41], 15-Hz
low-pass-filtered MEG was used for decoding, and it was
reported that the sensor networks over central and parietal
scalp areas contributed more toward this decoding. Further
studies have used MEG to reconstruct 2-D fingertip movement trajectory [42] to decode movement velocity [43],
[44]. The 2-D or three-dimensional (3-D) center-out movement/reaching tasks were used to reconstruct kinematics
using low-frequency MEG. In [45], an fNIRS signal was
used to estimate finger pinch forces using a sparse linear
regression method. The applicability of EEG in decoding
hand-movement kinematics was explored in [46] and [47]
using a center-out 3-D hand-movement experiment



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