Girijesh Prasad

Ulster University · School of Computing and Intelligent Systems

Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain-computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants im...

The blood-oxygen label dependent (BOLD) signal obtained from functional magnetic resonance images (fMRI) varies significantly among populations. Yet, there is some agreement among researchers over the pace of the blood flow within several brain regions relative to the subject’s age and cognitive ability. Our analysis further suggested that regional...

Accurate mapping of cortical engagement during mental imagery or cognitive tasks remains a challenging brain–imaging problem with immediate relevance to the development of brain – computer interfaces (BCI). We analyzed data from fourteen individuals who performed cued motor imagery, mental arithmetic, or silent word generation tasks during MEG reco...

Current machine learning techniques for dementia diagnosis often do not take into account real-world practical constraints, which may include, for example, the cost of diagnostic assessment time and financial budgets. In this work, we built on previous cost-sensitive feature selection approaches by generalising to multiple cost types, while taking...

Current machine learning techniques for dementia diagnosis often do not take into account real-world practical constraints, which may include, for example, the cost of diagnostic assessment time and financial budgets. In this work, we built on previous cost-sensitive feature selection approaches by generalising to multiple cost types, while taking...

Objective Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosi...

Accurate computational models for clinical decision support systems require clean and reliable data but, in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. This work addresses the problem of extreme m...

Children with dyslexia need specific instructions for spelling and word analysis from an early age. It is important to provide appropriate tools using technology for writing aids to such children that can help them to input text, while providing multiple feedback. However, it is unclear how children with dyslexia can efficiently use a gaze-based vi...

Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzhei-mer's disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do not fully utilize the two available MEG modalities (extracted using magnetometer and gradiometer sens...

Background Peri-conceptional folic acid (FA) supplementation is known to prevent neural tube defects. It remains uncertain whether continuing FA after the first trimester has benefits for offspring neurodevelopment. A previously published follow up study of Mothers who had participated in a randomized trial of FA Supplementation in the Second and T...

Background Peri-conceptional folic acid(FA) supplementation is known to prevent neural tube defects. It remains uncertain whether continuing FA after the first trimester has benefits for offspring development. A previously published follow up study of Mothers who had participated in a randomized trial of FA Supplementation in the Second and Trimest...

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discrimin...

Previous studies have demonstrated that musical deviants (syntactically irregular chords) elicit event related potentials/fields with negative polarity; specifically, the early right anterior negativity and the right anterior temporal negativity responses with peak latencies at ~200 ms and ~350 ms, respectively, post stimulus onset. Here, we invest...

Emotion processing has been a focus of research in psychology and neuroscience for some decades. While the evoked neural markers in human brain activations in response to different emotions have been reported, the temporal dynamics of emotion processing has received less attention. Differences in processing speeds, that depend on emotion type, have...

Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily av...

A motor imagery (MI) based brain-computer interface (BCI) decodes the motor intention from the electroencephalogram (EEG) of a subject and translates this into a control signal. These intentions are hence classified as different cognitive tasks, e.g. left and right hand movements. A challenge in developing a BCI is handling the high dimensionality...

Background Maternal folic acid (FA) supplementation before and in early pregnancy prevents neural tube defects (NTD), but it is uncertain whether continuing FA after the first trimester has benefits on offspring health. We aimed to evaluate the effect of FA supplementation throughout pregnancy on cognitive performance and brain function in the chil...

Background : Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing. New metho...

Background: Systems Medicine is a novel approach to medicine, i.e. an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral...

Objective: Magnetoencephalography (MEG) based brain-computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computatio...

Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to motor imagery (MI). The recent success of deep learning-based algorithms in classifying different brain signals warrants further exploration to deter...

The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study esta...

Dementia is a collection of symptoms associated with impaired cognition and impedes everyday normal functioning. Dementia, with Alzheimer’s disease constituting its most common type, is highly complex in terms of etiology and pathophysiology. A more quantitative or computational attitude towards dementia research, or more generally in neurology, is...

Generative Adversarial Networks (GAN) have led to important advancements in generation of time-series data in areas like speech processing. This ability of GANs can be very useful for Brain-Computer Interfaces (BCIs) where collecting large number of samples can be expensive and time-consuming. To address this issue, this paper presents a new approa...

Accurate computational models for clinical decision support systems require clean and reliable data, but in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. Many popular methods of handling missing dat...

Data imputation is the most popular method of dealing with missing values, but in most real life applications, large missing data can occur and it is difficult or impossible to evaluate whether data has been imputed accurately (lack of ground truth). This paper addresses these issues by proposing an effective and simple principal component based me...

Data imputation is the most popular method of dealing with missing values, but in most real life applications, large missing data can occur and it is difficult or impossible to evaluate whether data has been imputed accurately (lack of ground truth). This paper addresses these issues by proposing an effective and simple principal component based me...

Periconceptional folic acid (FA) has an established role in the prevention of neural tube defects (NTDs), leading to global recommendations for FA supplementation before and in early pregnancy. However, it is unclear whether there are any benefits for offspring brain health arising from continued maternal FA supplementation beyond the first trimest...

Dementia is a collection of symptoms associated with impaired cognition and impedes everyday normal functioning. Dementia, with Alzheimer's disease constituting its most common type, is highly complex in terms of etiology and pathophysiology. A more quantitative or computational attitude towards dementia research, or more generally in neurology, is...

Brain functional connectivity (FC) analyses based on magnetoencephalographic (MEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, which leads to conservative hypothesis testing. We removed such constraint by extending clus...

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily...

The performance of a brain-computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier's generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibrati...

The recent development of inexpensive and accurate eye-trackers allows the creation of gazed based virtual keyboards that can be used by a large population of disabled people in developing countries. Thanks to eye-tracking technology, gaze-based virtual keyboards can be designed in relation to constraints related to the gaze detection accuracy and...

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have...

The electroencephalogram (EEG) signals tend to have poor time-frequency localization when analysis techniques involve a fixed set of basis functions such as in short-time Fourier transform (STFT) and wavelet transform (WT). These signals also exhibit highly non-stationary characteristics and suffer from low signal-to-noise ratio (SNR). As a result,...

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on the group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heav...

The usability of virtual keyboard based eye-typing systems is currently limited due to the lack of adaptive and user-centered approaches leading to low text entry rate and the need for frequent recalibration. In this work, we propose a set of methods for the dwell time adaptation in asynchronous mode and trial period in synchronous mode for gaze ba...

A brain–computer interface (BCI) aims to facilitate a new communication path that translates the motion intentions of a human into control commands using brain signals such as magnetoencephalography (MEG) and electroencephalogram (EEG). In this work, a comparison of features obtained using single channel and multichannel empirical mode decompositio...

The brain’s functional connectivity (FC) estimated at sensor level from electromagnetic (EEG/MEG) signals can provide quick and useful information towards understanding cognition and brain disorders. Volume conduction (VC) is a fundamental issue in FC analysis due to the effects of instantaneous correlations. FC methods based on the imaginary part...

Background Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primari...

This paper presents an underactuated design of a robotic hand exoskeleton and a challenge based neurorehabilitation strategy. The exoskeleton is designed to reproduce natural human fingertip paths during extension and grasping, keeping minimal kinematic complexity. It facilitates an impedance adaptation based trigged assistance control strategy by...

Every year approximately 1.5 million of the European population suffer a stroke and more than 70% of stroke survivors experience limited functional recovery of their upper limb, resulting in diminished quality of life. Therefore, interventions to address upper-limb impairment are a priority for stroke survivors and clinicians. Recently, various inn...

Appropriately combining mental practice (MP) and physical practice (PP) in a post-stroke rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here we present a rehabilitation protocol incorporating a separate active PP stage followed by MP stage, using a hand exoskeleton and brain-computer interface (BCI). The PP...

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have...

Up to 15% of the Indian school-going children suffer from dyslexia. This paper aims to determine the extent to which existing knowledge about the eye-tracking based human-computer interface can be used to assist these children in their reading and writing activities. A virtual keyboard system with multimodal feedback is proposed and designed for a...

A large number of people with disabilities rely on assistive technologies to communicate with their families, to use social media, and have a social life. Despite a significant increase of novel assitive technologies, robust, non-invasive, and inexpensive solutions should be proposed and optimized in relation to the physical abilities of the users....

connectivity measurements can provide key information about ongoing brain processes. In this paper, we propose to investigate the performance of the binary classification of Propofol-induced sedation states using partial granger causality analysis. Based on the brain connectivity measurements obtained from EEG signals in a database that contains fo...

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approa...

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and ambient assisted living for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical...

Majority of the current Brain-Computer Interface (BCI) related studies involved electroencephalography (EEG) recordings. Although several studies utilized magnetoencephalography (MEG) for BCI application, their analyses have been focused on motor imagery (MI) tasks only. Recently, MEG acquired enhanced attention for neural engineering applications...

Introduction: Dementia, with Alzheimer’s disease (AD) being its most common form, is one of the most important contributors to dependence and disability of older people and the focus of growing clinical research interest. Along with the intensive search for interventions that can modify progression of dementia symptoms, researchers investigate vari...

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and...

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and...

Every year approximately 1.5 million of the European population suffer a stroke and more than 70% of stroke survivors experience limited functional recovery of their upper limb, resulting in diminished quality of life. Therefore, interventions to address upper-limb impairment are a priority for stroke survivors and clinicians. Recently, various inn...