IEEE Systems, Man and Cybernetics Magazine - April 2018 - 17
Table 1. An overview of the state of the art in EEG source analysis
of movement-type classification.
Key Findings [Classification
Accuracies (CAs)]
Study
Type of Experiment
Nch
Inverse Methods
Features and Classifier
[8]
Left- versus right-hand MI
59
EDA, CCD
Complex Morlet wavelets features
CA of 78.9% (EDA), 80.6%
(CCD)
[20]
Left- versus right-hand MI
59
CCD
Von Neumann relative entropy
features
CA of 88% (CCD)
[21]
BCI Competition 2003
Data Set IV [22]: SelfPaced Tapping (SPT)
28
sLORETA
Data-driven spatial-filter plus sLORETA
source power in left and right ROIs;
classification based on a simple
decision tree
CA of 83% (sLORETA plus
spatial filter); 78% (sLORETA, no
spatial filter)
[23]
Left- versus right-hand MI
16
FD-MNE
TF combination with lowest p-values
CA of 71% (trained group)
[24]
Data Set IV [22]: SPT;
Data Set IIIa [25]
60
sLORETA
Fuzzy Region of Interest Activity
(FuRIA)-based features; classification
using OVR-SVM
CA of 84% (Data Set IV), CA of
82.4% (Data Set IIIa)
[26]
BCI Comp. 2003 Data Set
IV: SPT
28
LORETA
Change in power spectral density
(PSD) in 1-40 Hz and Bereitschaftspotential features
CA of 84.25%
[27]
Error-potential paradigm
(left-hand versus foot MI)
64
sLORETA
PSD features from sensor-space are
classified using a simple Gaussian
classifier
CA of 81.8% (correct trials)
[28]
MI-based cursor control
in left versus right (Graz
approach)
128
Beamformer
Log-bandpower of 20 frequency
bands; spatial filters obtained using
static beamformer
Mean CA of 79.8%
[29]
Left versus right index
finger tapping (ME)
128
LAURA
Single-trial discriminative power
features classified using LOO-SVM
cross-validation
Average CA of 97% over 12
subjects
[30]
MI-based cursor control
64
LORETA
FuRIA features and adaptive FLD
classifier
Average CA of 85%
[31]
Left- versus right-hand MI
64
MNE
Morlet wavelets-based TF selection
(6-30 Hz) followed by ERD/S,
Source R > scalp R
[32]
Self-initiated movement
to one of the eight directions
128
sLORETA
Linear decoding model to fit the
3-D coordinates of hand movement with EEG
Continuous decoding of
hand velocity from EEG is
demonstrated
[33]
MI-based cursor control
in up, down, left, and
right
128
sLORETA
Regression weights of the linear
decoding model multiplied by
time-series of voxels
Continuous decoding of hand
MI with minimal calibration
[34]
Right-hand movement in
four directions
118
wMNE
Features from regularized variants of
CSP followed by OVR SVM
Four-class CA of source-space
SRCSP (64.98%) > sensorspace (52.20%)
[35]
Right-hand movement in
four directions
118
sLORETA
Features from supervised factor
analysis (SFA) followed by OVR-SVM
Four-class CA of source-space
SFA features (71%)
Comp.: competition; EDA: equivalent dipole analysis; CCD: cortical current density; OVR: one-versus-rest; SVM: support vector machine; LOO: leave-one-out; FLD:
Fisher's linear discriminant; ERD/S: event-related desynchronization/synchronization; CSP: common spatial patterns; SRCSP: shrinkage-regularized CSPs.
ipsilateral superior parietal lobule, and precuneus were
involved in the encoding of the hand velocity in all phases.
Although [59] was an MEG-based study, the same sourceimaging approach can also be extended to EEG.
In an EEG-based gait speed study [60], independent
components clustering, as well as equivalent dipole
localization based on brain electrical source analysis
(BESA), in a four-shell spherical model revealed that the
posterior parietal cortex contains information regarding gait speed. Although an invasive study, we find it
worth highlighting [61], which suggests the speed-related information is well represented in the neural activity
as compared to direction-related information. It is
worth noting that the low-frequency cortical source
Ap ri l 2018
IEEE SyStEmS, man, & CybErnEtICS magazInE
17
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