Signal Processing - September 2017 - 192
should become more similar the more
they are entraining. Specifically, data
space similarity between two people was
quantified through the temporal convergence between their corresponding PCA
axes, i.e., through the angles formed by
each pair of these principal directions:
ssim (X 1, X 2) = trace (W T1L W 2L W T2L W 1L)
k
=
k
/ / cos 2 (i ij),
(1)
i= 1 j= 1
where X 1, X 2 represent expressed behaviors of speakers 1 and 2, and W 1L
and W 2L correspond to the reduced rank
projection matrix of each individual toward their signal spaces; i ij is the angle
formed between the ith principal component of the data space from participant 1 and the jth principal component
from participant 2. Lee et al. showed
that this signal processing-derived synchrony measure related to couples' attachment styles and in extension can be
a good indicator of conflict episodes.
Connection between physiology
and the brain
The true internal state can be thought of as
a particular configuration of brain functioning resulting from complex neural activities within. Proxy measures of neural
activities can now be captured and processed via advances in brain activity sensing, e.g., the recently prevalent functional
magnetic resonance imaging (fMRI), and
widely used electrophysiological signals
(electroencephalography, magnetoencephalography, electromyography, etc.)
Each of these signals is a loaded signal,
differing in spatial-temporal resolution
and utility as a proxy measure of human
brain activity. Many of these signals require advanced signal processing methodologies to provide a valid objective
representation on a particular neural functioning from the raw data. Additionally,
these measures need to be further conditioned on appropriate expert-designed
stimulation protocols (i.e., controlling
factors to address the extreme variability
within human brain).
Capturing relationships between
brain functioning, physiological responses, and expressive behaviors is
crucial in objectively piecing together
192
the intricate interplay the between mind
and body, and it relies on signal processing. Previous studies typically address
these three targets separately, but signal processing will become the "glue"
between them. For instance, one may
simultaneously understand the effects
of signal context (e.g., a high-blaming
tone of voice) as the trigger of emotional
stimuli in changing the brain/physiological responses (measured through another set of quantitative proxy metrics)
as well as the resulting reaction (e.g.,
a stressed, annoyed facial expression).
Modeling the timing, coordination, and
appropriateness of each in relation to
another with foundational mathematics
will introduce insights that are not easily
obtained as three isolated components.
Clinical opportunities:
Enhancing human perception,
cognition, and action
The initial step in conducting interdisciplinary research in behavioral science
is to consider what the outcome of the
research will be. There are two primary
computational approaches: predicting a label or generating a rule-based
definition of a label (e.g., the entrainment measure discussed previously).
What makes this interdisciplinary research even more challenging is the
researcher often desires his or her work
to be both computationally meaningful
and relevant to the application domain;
however, even a simple, interpretable
computational system may be revolutionary for the behavioral science domain, but might necessitate a reduced
computational complexity. Nevertheless, given the promise of signal processing for mental health, we provide a
short overview of certain clinical opportunities, with computation ranging from
simple feature extraction to end-to-end
human-in-the-loop systems.
Screening and diagnosis
For engineers, a straightforward application of technical know-how is to classify
data, which in this case means diagnosing or screening for a disorder. In some
cases, this may be a viable approach.
However, many signal processing techniques are currently limited and trail far
IEEE SIGNAL PROCESSING MAGAZINE
|
September 2017
|
behind human perception and judgment,
which means humans have a firm hold
as the gold standard, and computational
techniques ought to support their efforts.
Consider image processing: the world's
leading experts have only recently been
able to robustly identify animals within
a photograph; but transfer learning of
those models are currently being applied
to static medical image-based diagnosis. Yet human behavior is much more
complex, nuanced, and dynamic than a
static image, so we assert that machines
have a long distance to go in mimicking
human perception and action, although
there are tantalizing possibilities in store
for the future.
One viable application of machine
learning has been in developing robust
diagnostic and screening algorithms.
Traditionally this has been done through
both hand-chosen features and statistical analysis, which does not optimize the
desired objective function directly-the
objective function is a combination of
sensitivity (recall) and specificity (true
negative rate). Machine learning is a
perfect fit! Recent work in autism diagnostics has shown that machine-learning
based algorithms can effortlessly fuse
coded behaviors from multiple diagnostic instruments, are tunable, and can
easily reduce the total coded behavior set
(feature reduction), effectively shortening
the administration time [19].
Taking another angle, consider the
case of "atypical" prosody, a prevalent
behavioral characteristic marker in neural and motor disorders, which has been
said to be the most consistent marker of
autism across the life span, although it
varies with age and language level and
across individuals. Autism researchers
are currently constrained to analyze
very small amounts of data with meticulous, time-consuming coding; but
that coding is often unreliable. One of
the primary research thrusts of Bone et
al. has been to provide a computational
definition of prosody for developmental
disorders that could be used in conjunction with human perception of other
symptoms [15]. For either purely datadriven or expert systems, one of the
greatest advantages may be for monitoring behavior over long periods of time.
Table of Contents for the Digital Edition of Signal Processing - September 2017
Signal Processing - September 2017 - Cover1
Signal Processing - September 2017 - Cover2
Signal Processing - September 2017 - 1
Signal Processing - September 2017 - 2
Signal Processing - September 2017 - 3
Signal Processing - September 2017 - 4
Signal Processing - September 2017 - 5
Signal Processing - September 2017 - 6
Signal Processing - September 2017 - 7
Signal Processing - September 2017 - 8
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Signal Processing - September 2017 - 20
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Signal Processing - September 2017 - 26
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Signal Processing - September 2017 - 28
Signal Processing - September 2017 - 29
Signal Processing - September 2017 - 30
Signal Processing - September 2017 - 31
Signal Processing - September 2017 - 32
Signal Processing - September 2017 - 33
Signal Processing - September 2017 - 34
Signal Processing - September 2017 - 35
Signal Processing - September 2017 - 36
Signal Processing - September 2017 - 37
Signal Processing - September 2017 - 38
Signal Processing - September 2017 - 39
Signal Processing - September 2017 - 40
Signal Processing - September 2017 - 41
Signal Processing - September 2017 - 42
Signal Processing - September 2017 - 43
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Signal Processing - September 2017 - 45
Signal Processing - September 2017 - 46
Signal Processing - September 2017 - 47
Signal Processing - September 2017 - 48
Signal Processing - September 2017 - 49
Signal Processing - September 2017 - 50
Signal Processing - September 2017 - 51
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Signal Processing - September 2017 - 53
Signal Processing - September 2017 - 54
Signal Processing - September 2017 - 55
Signal Processing - September 2017 - 56
Signal Processing - September 2017 - 57
Signal Processing - September 2017 - 58
Signal Processing - September 2017 - 59
Signal Processing - September 2017 - 60
Signal Processing - September 2017 - 61
Signal Processing - September 2017 - 62
Signal Processing - September 2017 - 63
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Signal Processing - September 2017 - 65
Signal Processing - September 2017 - 66
Signal Processing - September 2017 - 67
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Signal Processing - September 2017 - 69
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Signal Processing - September 2017 - 71
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Signal Processing - September 2017 - 82
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Signal Processing - September 2017 - 84
Signal Processing - September 2017 - 85
Signal Processing - September 2017 - 86
Signal Processing - September 2017 - 87
Signal Processing - September 2017 - 88
Signal Processing - September 2017 - 89
Signal Processing - September 2017 - 90
Signal Processing - September 2017 - 91
Signal Processing - September 2017 - 92
Signal Processing - September 2017 - 93
Signal Processing - September 2017 - 94
Signal Processing - September 2017 - 95
Signal Processing - September 2017 - 96
Signal Processing - September 2017 - 97
Signal Processing - September 2017 - 98
Signal Processing - September 2017 - 99
Signal Processing - September 2017 - 100
Signal Processing - September 2017 - 101
Signal Processing - September 2017 - 102
Signal Processing - September 2017 - 103
Signal Processing - September 2017 - 104
Signal Processing - September 2017 - 105
Signal Processing - September 2017 - 106
Signal Processing - September 2017 - 107
Signal Processing - September 2017 - 108
Signal Processing - September 2017 - 109
Signal Processing - September 2017 - 110
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Signal Processing - September 2017 - 112
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Signal Processing - September 2017 - 127
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Signal Processing - September 2017 - 131
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Signal Processing - September 2017 - 138
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Signal Processing - September 2017 - 140
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Signal Processing - September 2017 - 143
Signal Processing - September 2017 - 144
Signal Processing - September 2017 - 145
Signal Processing - September 2017 - 146
Signal Processing - September 2017 - 147
Signal Processing - September 2017 - 148
Signal Processing - September 2017 - 149
Signal Processing - September 2017 - 150
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Signal Processing - September 2017 - 153
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Signal Processing - September 2017 - 195
Signal Processing - September 2017 - 196
Signal Processing - September 2017 - Cover3
Signal Processing - September 2017 - Cover4
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