25 research outputs found
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
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
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
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
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
An exploration of EEG features during recovery following stroke – implications for BCI-mediated neurorehabilitation therapy
Background: Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a
limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is
known about the technical requirements for the design of a rehabilitative BCI for stroke.
Methods: 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy
subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design
based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and
accuracy of training a rehabilitative BCI with both stroke-affected and healthy data.
Results: Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When
training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for
healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the
corresponding early stroke EEG dataset.
Conclusions: This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated
with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part
of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural
measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we
believe motor retraining BCI should initially be tailored to individual patients
Investigations into Brain-Computer Interfacing for Stroke Rehabilitation
A stroke is the loss of brain function following the cessation of blood supply
to a region of the brain caused by either a blockage or haemorrhage in the
vasculature. It is a leading cause of death worldwide but survival rates have
increased significantly in the past 25 years with recent estimates putting the
number of worldwide stroke survivors at 33 million. Stroke survivors live
with lasting effects such as limb weakness, limb paralysis, loss of speech,
loss of comprehension and other neurological disorders. The purpose of
stroke rehabilitation is to return the sufferer to as normal a life as possible.
Traditional methods for this involve mass practice of the affected function to
provoke improvement, acquisition of compensatory skills and adaptation to
residual post-stroke disability. Recently, however, brain computer interfaces
(BCI) have emerged as a technology which may have impact in augmenting
traditional approaches, particularly for motor deficits. In this context, BCI
provides a means for closing the sensorimotor loop and driving neuroplastic
processes to enhance recovery.
A BCI is a system for translating measured brain activity into control signals
for an external device, such as a computer or machine. Rehabilitation
BCI attempts to use such a device to encourage positive neurorehabilitation
in the stroke survivor, to return or strengthen lost or diminished
function. This thesis describes concerted work to improve the current state
and future prospects of rehabilitation BCI. In particular, BCIs which use
electroencephalography (EEG) and functional near-infrared spectroscopy
(fNIRS) to measure brain activity are the focus of these efforts. EEG and
fNIRS are relatively inexpensive, easy-to-use and portable brain measurement/
imaging systems compared to other brain imaging methods commonly
found in hospital settings, such as functional magnetic resonance imaging
(fMRI), positron emission tomography (PET) or magnetoencephalography
(MEG). These advantages motivate this research in the hope that at-home
stroke rehabilitation becomes widespread and the accepted method of stroke
rehabilitation.
Investigations described here include the design and development of a novel
fNIRS imaging method, a novel fNIRS synthetic data generation algorithm,
a novel hybrid fNIRS/EEG measurement system, a novel portable EEG
biofeedback BCI, a substantial investigation into the effect of stroke on
EEG BCI operation and performance, and an investigation into potential
biomarkers for neurorehabilitation based on BCI parameters and scalp EEG.
These investigations, based on measurements of both healthy and stroke affected brain activity, have lead to the advancement of EEG and fNIRSbased
rehabilitation BCI technology
Investigations into Brain-Computer Interfacing for Stroke Rehabilitation
A stroke is the loss of brain function following the cessation of blood supply
to a region of the brain caused by either a blockage or haemorrhage in the
vasculature. It is a leading cause of death worldwide but survival rates have
increased significantly in the past 25 years with recent estimates putting the
number of worldwide stroke survivors at 33 million. Stroke survivors live
with lasting effects such as limb weakness, limb paralysis, loss of speech,
loss of comprehension and other neurological disorders. The purpose of
stroke rehabilitation is to return the sufferer to as normal a life as possible.
Traditional methods for this involve mass practice of the affected function to
provoke improvement, acquisition of compensatory skills and adaptation to
residual post-stroke disability. Recently, however, brain computer interfaces
(BCI) have emerged as a technology which may have impact in augmenting
traditional approaches, particularly for motor deficits. In this context, BCI
provides a means for closing the sensorimotor loop and driving neuroplastic
processes to enhance recovery.
A BCI is a system for translating measured brain activity into control signals
for an external device, such as a computer or machine. Rehabilitation
BCI attempts to use such a device to encourage positive neurorehabilitation
in the stroke survivor, to return or strengthen lost or diminished
function. This thesis describes concerted work to improve the current state
and future prospects of rehabilitation BCI. In particular, BCIs which use
electroencephalography (EEG) and functional near-infrared spectroscopy
(fNIRS) to measure brain activity are the focus of these efforts. EEG and
fNIRS are relatively inexpensive, easy-to-use and portable brain measurement/
imaging systems compared to other brain imaging methods commonly
found in hospital settings, such as functional magnetic resonance imaging
(fMRI), positron emission tomography (PET) or magnetoencephalography
(MEG). These advantages motivate this research in the hope that at-home
stroke rehabilitation becomes widespread and the accepted method of stroke
rehabilitation.
Investigations described here include the design and development of a novel
fNIRS imaging method, a novel fNIRS synthetic data generation algorithm,
a novel hybrid fNIRS/EEG measurement system, a novel portable EEG
biofeedback BCI, a substantial investigation into the effect of stroke on
EEG BCI operation and performance, and an investigation into potential
biomarkers for neurorehabilitation based on BCI parameters and scalp EEG.
These investigations, based on measurements of both healthy and stroke affected brain activity, have lead to the advancement of EEG and fNIRSbased
rehabilitation BCI technology
Investigations into Brain-Computer Interfacing for Stroke Rehabilitation
A stroke is the loss of brain function following the cessation of blood supply
to a region of the brain caused by either a blockage or haemorrhage in the
vasculature. It is a leading cause of death worldwide but survival rates have
increased significantly in the past 25 years with recent estimates putting the
number of worldwide stroke survivors at 33 million. Stroke survivors live
with lasting effects such as limb weakness, limb paralysis, loss of speech,
loss of comprehension and other neurological disorders. The purpose of
stroke rehabilitation is to return the sufferer to as normal a life as possible.
Traditional methods for this involve mass practice of the affected function to
provoke improvement, acquisition of compensatory skills and adaptation to
residual post-stroke disability. Recently, however, brain computer interfaces
(BCI) have emerged as a technology which may have impact in augmenting
traditional approaches, particularly for motor deficits. In this context, BCI
provides a means for closing the sensorimotor loop and driving neuroplastic
processes to enhance recovery.
A BCI is a system for translating measured brain activity into control signals
for an external device, such as a computer or machine. Rehabilitation
BCI attempts to use such a device to encourage positive neurorehabilitation
in the stroke survivor, to return or strengthen lost or diminished
function. This thesis describes concerted work to improve the current state
and future prospects of rehabilitation BCI. In particular, BCIs which use
electroencephalography (EEG) and functional near-infrared spectroscopy
(fNIRS) to measure brain activity are the focus of these efforts. EEG and
fNIRS are relatively inexpensive, easy-to-use and portable brain measurement/
imaging systems compared to other brain imaging methods commonly
found in hospital settings, such as functional magnetic resonance imaging
(fMRI), positron emission tomography (PET) or magnetoencephalography
(MEG). These advantages motivate this research in the hope that at-home
stroke rehabilitation becomes widespread and the accepted method of stroke
rehabilitation.
Investigations described here include the design and development of a novel
fNIRS imaging method, a novel fNIRS synthetic data generation algorithm,
a novel hybrid fNIRS/EEG measurement system, a novel portable EEG
biofeedback BCI, a substantial investigation into the effect of stroke on
EEG BCI operation and performance, and an investigation into potential
biomarkers for neurorehabilitation based on BCI parameters and scalp EEG.
These investigations, based on measurements of both healthy and stroke affected brain activity, have lead to the advancement of EEG and fNIRSbased
rehabilitation BCI technology