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

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

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

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