IEEE Consumer Electronics Magazine - July 2015 - 38

Band Power Feature extraction

A comprehensive strategy for
dealing with noisy data could
also include data filtering and
an optimal selection of
geometric parameters, such
as sensor positioning.

1) band-pass filtering and power estimation taking tem2)
3)
4)
5)

poral average
periodogram (Fourier decomposition)
power spe ct ra l densit y f rom autoreg ressive
coefficients
wavelet scalogram (time-scale representation)
spectrogram (time-frequency decomposition with
averaged spectrums over time) [2008].

cSP analySiS
to a person's brain condition varies from group to group. In general, the interpretation states whether a person is in attention or
mediation. Exactly what attention and mediation means is left
for one to judge.
Various digital processing algorithms were attempted for
the purpose of making the brain signal interpretable via an
increase of the signal-to-noise ratio, manipulation of sensor
spatial and time domain parameters (TDPs), and fitting of a
priori knowledge and various assumptions to yield some performance improvement. These digital processing algorithms
are profiled as follows.

DIGITAL BRAIN SIGNAL PROCESSING ALGORITHMS
Major research efforts have been spent developing digital
processing algorithms to identify brain signals at various frequency bands. A plethora of inverse problem solving and pattern-matching analyses have been applied to signals
measured from many sensors placed on a cap noninvasively
covering a person's head. Stable reconstructions of brain-signal features could be achieved through the use of many techniques. Since the measurements and modeling techniques
both contain noise and assumptions, the true solution could
hardly be totally derived from the algorithms or totally determined from the measurements. A comprehensive strategy for
dealing with noisy data could also include data filtering and
an optimal selection of geometric parameters, such as sensor
positioning. One particularly notable technique suggested
was regularization [13], which attempted to achieve a compromise between a close fit to the data and stability of the
algorithmic solution. By removing the high-frequency component from a derived solution, it believed that it effectively
filtered a portion of the noise [13].
Various algorithms have demonstrated performances up to
90% accuracy in a controlled environment. They could be highly
computationally intensive. They were primarily carried out
offline in batch processing, with unproven real-time applications. They also required stringent calibration and testing to
facilitate performance dedicated to an individual. To appreciate
the level of effort spent in the development of these brain signal
processing algorithms, three major categories are elaborated:
1) band-power feature extraction, 2) common spatial patterns
(CSP) analysis, and 3) statistical source separation [6]. The year
cited, spanning from 1981 to 2014, refers to approximately the
first introduction of the algorithm.
38 IEEE Consumer Electronics Magazine

^

july 2015

1) spatial filtering (SF)
i) bipolar
ii) Laplacian
2) physical forward modeling: inverse solution methods
i) Minimum current estimate [2008, 2009]
ii)

iii)

iv)
v)

vi)

- Focal underdetermined system solver [1995]
Weighted minimum norm estimate
- LORETA, eLORETA, and sLORETA-all
assuming smoothness [1987, 1994]
Mixed norm estimate, combining sparsity and
smoothness [2008]
- S-FLEX Champagne-simple spatial structure
[2011]
minimum entropy
source localization paradigms
- dipole modeling [1992]
- multipole modeling
- scanning
a) subspace methods (MUSIC/RAP-MUSIC)
[1986, 1999]
b) beamformers [1997]
i) LCMV beamformer
ii) nulling beamformer
depth compensation modeling [1987]
c) CSP [1990]
i) supervised regulated spatial filters based
on EEG and CSP
ii) filter bank CSP
iii) discriminatory filter (DFCSP)
iv) sparse CSP
v) source power correlation analysis (SPoC)
[2014]
vi) canonical spatial power comodulation [2014]
vii) steady-state auditory evoked potentials [1981]
viii) steady-state visual evoked potentials
ix) spatiospectral decomposition.

StatiStical Backward Modeling:
Blind Source SeParation
1) linear classifiers-linear discriminant analysis (LDA),

support vector machine (SVM), and infinite impulse
response [2010]
2) linear regression-OLS, ridge regression, LASSO
[2005]
3) principal component analysis (PCA) [2005]



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