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Limited By:	Author = Leamy, Darren J.;

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A novel co-locational and concurrent fNIRS/EEG measurement system: design and initial results. (2010)

Leamy, Darren J.; Ward, Tomas E.

A novel co-locational and concurrent fNIRS/EEG measurement system: design and initial results. (2010)

Leamy, Darren J.; Ward, Tomas E.

Abstract:

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 ...

http://mural.maynoothuniversity.ie/4162/

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An exploration of EEG features during recovery following stroke – implications for BCI-mediated neurorehabilitation therapy (2014)

Leamy, Darren J.; Kocijan, Jus; Domijan, Katarina; Duffin, Joseph; Roche, Richard; Comm...

An exploration of EEG features during recovery following stroke – implications for BCI-mediated neurorehabilitation therapy (2014)

Leamy, Darren J.; Kocijan, Jus; Domijan, Katarina; Duffin, Joseph; Roche, Richard; Commins, Sean; Collins, Ronan; Ward, Tomas E.

Abstract:

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. Classificati...

http://mural.maynoothuniversity.ie/6074/

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Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Abstract:

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 meas...

http://mural.maynoothuniversity.ie/4365/

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Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task (2011)

Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.

Abstract:

Included in Presentation

http://mural.maynoothuniversity.ie/4182/

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Functional Near Infrared Spectroscopy (fNIRS) synthetic data generation (2011)

Leamy, Darren J.; Ward, Tomas E.; Sweeney, Kevin

Functional Near Infrared Spectroscopy (fNIRS) synthetic data generation (2011)

Leamy, Darren J.; Ward, Tomas E.; Sweeney, Kevin

Abstract:

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 tempora...

http://mural.maynoothuniversity.ie/1476/

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Intelligent Artifact Classification for Ambulatory Physiological Signals (2010)

Sweeney, Kevin; Leamy, Darren J.; Ward, Tomas E.; McLoone, Sean F.

Intelligent Artifact Classification for Ambulatory Physiological Signals (2010)

Sweeney, Kevin; Leamy, Darren J.; Ward, Tomas E.; McLoone, Sean F.

Abstract:

Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents one of the most challenging signal acquisition challenges of all in that data is collected as the patient engages in normal activities of everyday living. Data thus collected suffers from considerable corruption as a result of artifact, much of it induced by motion and this has a bearing on its utility for diagnostic purposes. We propose a model for ambulatory signal recording in which the data collected is accompanied by labeling indicating the quality of the collected signal. As motion is such an important source of artifact we demonstrate the concept in this case with a quality of...

http://mural.maynoothuniversity.ie/3857/

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Investigations into Brain-Computer Interfacing for Stroke Rehabilitation (2015)

Leamy, Darren J.

Investigations into Brain-Computer Interfacing for Stroke Rehabilitation (2015)

Leamy, Darren J.

Abstract:

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 prov...

http://mural.maynoothuniversity.ie/6519/

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Using Gaussian Process Models for Near-Infrared Spectroscopy Data Interpolation (2010)

Leamy, Darren J.; Ward, Tomas E.; Kocijan, Jus

Using Gaussian Process Models for Near-Infrared Spectroscopy Data Interpolation (2010)

Leamy, Darren J.; Ward, Tomas E.; Kocijan, Jus

Abstract:

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 co...

http://mural.maynoothuniversity.ie/3861/