Jose Bornot | University of Ulster (original) (raw)

Papers by Jose Bornot

arXiv (Cornell University), Aug 26, 2022

Procedia - Social and Behavioral Sciences, 2013

Methods and Applications, 2014

State-space models are used in many research fields where dynamics are unobserved. Popular method... more State-space models are used in many research fields where dynamics are unobserved. Popular methods such as Kalman filtering and expectation maximization enable the estimation of these models but have a high computational cost in large-scale analysis. In such approaches, sparse inverse covariance estimators can reduce the cost; however, a trade-off between enforced sparsity and increased estimation bias occurs, which demands careful consideration in low signal-to-noise ratio scenarios. We overcome these limitations by 1) Introducing multiple penalised state-space (MPSS) models based on data-driven regularisation; 2) Solving MPSS models with novel algorithms extended from backpropagation, state-space gradient descent, or alternating least squares; 3) Proposing an extension of K-fold cross-validation to evaluate the regularisation parameters. Finally, we use MPSS models to solve the simultaneous brain source localisation and functional connectivity problems for simulated and real MEG/E...

2020 31st Irish Signals and Systems Conference (ISSC)

arXiv: Neurons and Cognition, Feb 21, 2019

Previous studies have demonstrated that musical deviants (syntactically irregular chords) elicit ... more 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 investigated differences in the neural dynamics of the auditory perceptual system of individuals with music training compared to those with no music training. Magnetoencephalography was used to examine the neural response to a deviant sound when the auditory system was primed using stimulus entrainment to evoke an auditory gamma-band response between 31 Hz and 39 Hz, in 2 Hz steps. Participants responded to the harmonic relationship between the entrainment stimulus and the subsequent target stimulus. Gamma frequencies carry stimulus information; thus, the paradigm primed the auditory system with a known gamma frequency and evaluated any improvem...

arXiv: Applications, 2019

We propose a modified Newton-Raphson (MNR) algorithm to estimate multiple penalized least squares... more We propose a modified Newton-Raphson (MNR) algorithm to estimate multiple penalized least squares (MPLS) models, and its extension to perform efficient optimization over the active set of selected features (AMNR). MPLS models are a more flexible approach to find adaptive least squares solutions that can be simultaneously required to be sparse and smooth. This is particularly important when addressing real-life inverse problems where there is no ground truth available, such as electrophysiological source imaging. The proposed MNR technique can be interpreted as a generalization of the Majorize-Minimize (MM) algorithm to include combinations of constraints. The AMNR algorithm allows to extend some penalized least squares methods to the p much greater than n case, as well as considering sign constraints. We show that these algorithms provide solutions with acceptable reconstruction in simulated scenarios that do not cope with model assumptions, for low n/p ratios. We then use both algo...

arXiv: Neurons and Cognition, 2019

We studied the interactions between different temporal scales of diffusion processes on complex n... more We studied the interactions between different temporal scales of diffusion processes on complex networks and found them to be stronger in scale-free (SF) than in Erdos-Renyi (ER) networks, especially for the case of phase-amplitude coupling (PAC)-the phenomenon where the phase of an oscillatory mode modulates the amplitude of another oscillation. We found that SF networks facilitate PAC between slow and fast frequency components of the diffusion process, whereas ER networks enable PAC between slow-frequency components. Nodes contributing the most to the generation of PAC in SF networks were non-hubs that connected with high probability to hubs. Additionally, brain networks from healthy controls (HC) and Alzheimer's disease (AD) patients presented a weaker PAC between slow and fast frequencies than SF, but higher than ER. We found that PAC decreased in AD compared to HC and was more strongly correlated to the scores of two different cognitive tests than what the strength of funct...

References o We have explored an application of the empirical mode decomposition (EMD) based filt... more References o We have explored an application of the empirical mode decomposition (EMD) based filtering method for enhancing performance of wrist movements in brain-computer interface (BCI). o The proposed method identifies a combination of IMFs whose maximum frequency falls in the low frequency band (<8 Hz). o It has provided improvement in the accuracy with sample entropy feature to classify multi direction wrist movement signals as compared to BCI competition winners.

Brain Informatics, 2021

Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the... more Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’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 sensors). AD consists of several stages of progression, this study addresses this limitation by using both magnetometer and gradiometer data to discriminate between participants with AD, AD-related mild cognitive impairment (MCI), and healthy control (HC) participants in the form of a three-class classification problem. A series of wavelet-based biomarkers are developed and evaluated, which concurrently leverage the spatial, frequency and time domain characteristics of the signal. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce interpretation of the brain activity captured by magnetometers ...

Sensors, 2021

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have ... more 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 discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted fr...

BMC Medicine, 2020

Background Dementia is caused by a variety of neurodegenerative diseases and is associated with a... more Background Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making. Main body Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practica...

2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), 2014

An electroencephalographic (EEG) waveform could be denoted by a series of ordinal patterns called... more An electroencephalographic (EEG) waveform could be denoted by a series of ordinal patterns called motifs which are based on the ranking values of subsequence time series. Permutation entropy (PE) has been developed to describe the relative occurrence of each of these motifs. However, PE has few limitations, mainly its inability to differentiate between distinct patterns of a certain motif, and its sensitivity to noise. To minimize those limitations, Weighted-Permutation Entropy (WPE) was proposed as a modification version of PE to improve complexity measuring for times series. This paper presents an approach by incorporating WPE into the analysis of different physiological states namely EEG time series. Three different EEG physiological states, eye-closed (EC), eye-open (EO), and visual oddball task (VOT) were included to examine ability of WPE to identify and discriminate different physiological states. The classification using WPE has achieved the results with accuracy of 87% between EC and EO states, and 83% between EO and VOT, respectively, using linear discrimination analysis. The results showed the potential of WPE to be a promising feature for nonlinear analysis in different physiological states of brain. It was also observed that WPE also could be used as marker for large artifact with low frequency such as eye-blink.