597 research outputs found
Application of the method of multiple scales to unravel energy exchange in nonlinear locally resonant metamaterials
In this paper, the effect of weak nonlinearities in 1D locally resonant
metamaterials is investigated via the method of multiple scales. Commonly
employed to the investigate the effect of weakly nonlinear interactions on the
free wave propagation through a phononic structure or on the dynamic response
of a Duffing oscillator, the method of multiple scales is here used to
investigate the forced wave propagation through locally resonant metamaterials.
The perturbation approach reveals that energy exchange may occur between
propagative and evanescent waves induced by quadratic nonlinear local
interaction
A novel co-locational and concurrent fNIRS/EEG measurement system: design and initial results.
We describe here the design, set-up and first time
classification results of a novel co-locational functional Near-
Infrared Spectroscopy/Electroencephalography (fNIRS/EEG)
recording device suitable for brain computer interfacing applications
using neural-hemodynamic signals. Our dual-modality
system recorded both hemodynamic and electrical activity at
seven sites over the motor cortex during an overt finger-tapping
task. Data was collected from two subjects and classified offline
using Linear Discriminant Analysis (LDA) and Leave-One-Out
Cross-Validation (LOOCV). Classification of fNIRS features,
EEG features and a combination of fNIRS/EEG features were
performed separately. Results illustrate that classification of the
combined fNIRS/EEG feature space offered average improved
performance over classification of either feature space alone.
The complementary nature of the physiological origin of the
dual measurements offer a unique and information rich signal
for a small measurement area of cortex. We feel this technology
may be particularly useful in the design of BCI devices for the
augmentation of neurorehabilitation therapy
Epistatic interactions of genes influence within-individual variation of physical activity traits in mice
A number of quantitative trait loci (QTLs) recently have been discovered that affect various activity traits in mice, but their collective impact does not appear to explain the consistently moderate to high heritabilities for these traits. We previously suggested interactions of genes, or epistasis, might account for additional genetic variability of activity, and tested this for the average distance, duration and speed run by mice during a 3 week period. We found abundant evidence for epistasis affecting these traits, although, recognized that epistatic effects may well vary within individuals over time. We therefore conducted a full genome scan for epistatic interactions affecting these traits in each of seven three-day intervals. Our intent was to assess the extent and trends in epistasis affecting these traits in each of the intervals. We discovered a number of epistatic interactions of QTLs that influenced the activity traits in the mice, the majority of which were not previously found and appeared to affect the activity traits (especially distance and speed) primarily in the early or in the late age intervals. The overall impact of epistasis was considerable, its contribution to the total phenotypic variance varying from an average of 22–35% in the three traits across all age intervals. It was concluded that epistasis is more important than single-locus effects of genes on activity traits at specific ages and it is therefore an essential component of the genetic architecture of physical activity
Functional Near Infrared Spectroscopy (fNIRS) synthetic data generation
Accurately modelled computer-generated data can
be used in place of real-world signals for the design, test
and validation of signal processing techniques in situations
where real data is difficult to obtain. Bio-signal processing
researchers interested in working with fNIRS data are restricted
due to the lack of freely available fNIRS data and by the
prohibitively expensive cost of fNIRS systems. We present a
simplified mathematical description and associated MATLAB
implementation of model-based synthetic fNIRS data which
could be used by researchers to develop fNIRS signal processing
techniques. The software, which is freely available, allows users
to generate fNIRS data with control over a wide range of
parameters and allows for fine-tuning of the synthetic data. We
demonstrate how the model can be used to generate raw fNIRS
data similar to recorded fNIRS signals. Signal processing steps
were then applied to both the real and synthetic data. Visual
comparisons between the temporal and spectral properties
of the real and synthetic data show similarity. This paper
demonstrates that our model for generating synthetic fNIRS
data can replicate real fNIRS recordings
Sex-, Diet-, and Cancer-Dependent Epistatic Effects on Complex Traits in Mice
The genetic basis of quantitative traits such as body weight and obesity is complex, with several hundred quantitative trait loci (QTLs) known to affect these and related traits in humans and mice. It also has become increasingly evident that the single-locus effects of these QTLs vary considerably depending on factors such as the sex of the individuals and their dietary environment, and we were interested to know whether this context-dependency also applies to two-locus epistatic effects of QTLs as well. We therefore conducted a genome scan to search for epistatic effects on 13 different weight and adiposity traits in an F2 population of mice (created from an original intercross of the FVB strain with M16i, a polygenic obesity model) that were fed either a control or a high-fat diet and half of which harbored a transgene (PyMT) that caused the development of metastatic mammary cancer. We used a conventional interval mapping approach with SNPs to scan all 19 autosomes, and found extensive epistasis affecting all of these traits. More importantly, we also discovered that the majority of these epistatic effects exhibited significant interactions with sex, diet, and/or presence of PyMT. Analysis of these interactions showed that many of them appeared to involve QTLs previously identified as affecting these traits, but whose single-locus effects were variously modified by two-locus epistatic effects of other QTLs depending on the sex, diet, or PyMT environment. It was concluded that this context-dependency of epistatic effects is an important component of the genetic architecture of complex traits such as those contributing to weight and obesity
Using Gaussian Process Models for Near-Infrared Spectroscopy Data Interpolation
Gaussian Process (GP) model interpolation is used
extensively in geostatistics. We investigated the effectiveness
of using GP model interpolation to generate
maps of cortical activity as measured by Near Infrared
Spectroscopy (NIRS). GP model interpolation also produces
a variability map, which indicates the reliability of
the interpolated data. For NIRS, cortical hemodynamic
activity is spatially sampled. When generating cortical
activity maps, the data must be interpolated. Popular NIRS
imaging software HomER uses Photon Migration Imaging
(PMI) and Diffuse Optical Imaging (DOI) techniques
based on models of light behaviour to generate activity
maps. Very few non-parametric methods of NIRS imaging
exist and none of them indicate the reliability of the interpolated
data. Our GP model interpolation algorithm and
HomER produced activity maps based on data generated
from typical functional NIRS responses. Image results
in HomER were taken as the bench mark as the images
produced are commonly considered to be representative of
the true underlying hemodynamic spatial response. The
output from the GP approach was then compared to these
on a qualitative basis. The GP model interpolation appears
to produce less structured image maps of hemodynamic
activity compared to those produced by HomER, however
a broadly similar spatial response is compelling evidence
of the utility of GP models for such applications. The additional
generation of a variability map which is produced
by the GP method may have some utility for functional
NIRS as such information is not explicitly available from
standard approaches. GP model interpolation can produce
spatial activity maps from coarsely sampled NIRS data
sets without any knowledge of the system being modelled.
While the images produced do not appear to have the
same feature resolution as photonic model-based methods
the technique is worthy of further investigation due to its
relative simplicity and, most intriguingly, its generation
of ancillary information in the form of the variability
map. This additional data may have some utility in NIRS
optode design or perhaps it may have application as
additional input for response classification purposes. This
GP technique may also be of use where model information
is inadequate for DOI techniques
Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task
Included in Presentatio
Functional Near Infrared Spectroscopy (fNIRS) synthetic data generation
Accurately modelled computer-generated data can
be used in place of real-world signals for the design, test
and validation of signal processing techniques in situations
where real data is difficult to obtain. Bio-signal processing
researchers interested in working with fNIRS data are restricted
due to the lack of freely available fNIRS data and by the
prohibitively expensive cost of fNIRS systems. We present a
simplified mathematical description and associated MATLAB
implementation of model-based synthetic fNIRS data which
could be used by researchers to develop fNIRS signal processing
techniques. The software, which is freely available, allows users
to generate fNIRS data with control over a wide range of
parameters and allows for fine-tuning of the synthetic data. We
demonstrate how the model can be used to generate raw fNIRS
data similar to recorded fNIRS signals. Signal processing steps
were then applied to both the real and synthetic data. Visual
comparisons between the temporal and spectral properties
of the real and synthetic data show similarity. This paper
demonstrates that our model for generating synthetic fNIRS
data can replicate real fNIRS recordings
Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task
This work serves as an initial investigation into improvements to classification accuracy of an imagined movement-based Brain Computer Interface (BCI) by combining the feature spaces of two unique measurement modalities: functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG). Our dual-modality system recorded concurrent and co-locational hemodynamic and electrical responses in the motor cortex during an imagined movement task, participated in by two subjects. Offline analysis and classification of fNIRS and EEG data was performed using leave-one-out cross-validation (LOOCV) and linear discriminant analysis (LDA). Classification of 2-dimensional fNIRS and EEG feature spaces was performed separately and then their feature spaces were combined for further classification. Results of our investigation indicate that by combining feature spaces, modest gains in classification accuracy of an imagined movement-based BCI can be achieved by employing a supplemental measurement modality. It is felt that this technique may be particularly useful in the design of BCI devices for the augmentation of rehabilitation therapy
- …