Computational Modelling of Spatio-Temporal EEG Brain Data with Spiking Neural Networks

The research presented in this thesis is aimed at modelling, classification and understanding of functional changes in brain activity that forewarn of the onset and/or the progression of a neurodegenerative process that may result in a number of disorders, including cognitive impairments, opiate addiction, Epilepsy and Alzheimer’s Disease. The study of neural plasticity and disease onset have been the centre of attention for researchers; especially as the population is ageing there is a need to deal with the increase in cognitive decline and the early onset of neurological diseases. As a consequence, large amounts of brain data has been collected and even more is expected to be collected, by means of novel computational techniques and biochemistry measurements. However, brain data is difficult to analyse and understand, especially since many of the traditional statistical and AI techniques are not able to deal with it appropriately. Driven by these issues and aiming to achieve the proposed goals, this study undertook to explore the potential of an evolving spatio-temporal data processing machine called the NeuCube architecture of spiking neurons, to analyse, classify and extract knowledge from electroencephalography spatio-temporal brain data. Firstly, the research undertaken in this thesis proposes a biologically plausible spiking neural network methodology for electroencephalography data classification and analysis. Secondly, it proposes a methodology for understanding functional changes in brain activity generated by the spatio-temporal data in the spiking neural network model. Thirdly, a new unsupervised learning rule is proposed for the investigation of the biological processes responsible for brain synaptic activity with the aim of targeting pharmacological treatments. The research undertaken achieved the following: high accuracy classification of electroencephalography data, even when fewer EEG channels and/or unprocessed data was used; personalised prognosis and early prediction of neurological events; the development of a tool for visualization and analysis of connectivity and spiking activity generated in the computational model; a better understanding of the impact of different drug doses on brain activity; a better understanding of specific neurological events by revealing the area of the brain where they occurred; and the analysis of the impact of biochemical processes on the neuronal synaptic plasticity of the model. Further improvement of the understanding and use of the proposed methodologies would contribute to the advancement of research in the area of prediction of neurological events and understanding of brain data related to neurological disorders, such as Alzheimer’s Disease

Adaptive long-term traffic state estimation with evolving spiking neural networks

Publisher Copyright: © 2019 Elsevier LtdDue to the nature of traffic itself, most traffic forecasting models reported in literature aim at producing short-term predictions, yet their performance degrades when the prediction horizon is increased. The scarce long-term estimation strategies currently found in the literature are commonly based on the detection and assignment to patterns, but their performance decays when unexpected events provoke non predictable changes, or if the allocation to a traffic pattern is inaccurate. This work introduces a method to obtain long-term pattern forecasts and adapt them to real-time circumstances. To this end, a long-term estimation scheme based on the automated discovery of patterns is proposed and integrated with an on-line change detection and adaptation mechanism. The framework takes advantage of the architecture of evolving Spiking Neural Networks (eSNN) to perform adaptations without retraining the model, allowing the whole system to work autonomously in an on-line fashion. Its performance is assessed over a real scenario with 5 min data of a 6-month span of traffic in the center of Madrid, Spain. Significant accuracy gains are obtained when applying the proposed on-line adaptation mechanism on days with special, non-predictable events that degrade the quality of their long-term traffic forecasts.This work has been supported by the Basque Government through the EMAITEK program, as well as by the Erasmus Mundus Action 2 PANTHER, “Pacific Atlantic Network for Technical Higher Education and Research”1 (Agreement No. 2013-5659/004-001 EMA2).Peer reviewe

Shaping the future: an Italian survey unveils the unmet need to empower physical medicine and rehabilitation professionals with technological skills

[no abstract available

Pengaruh motivasi dan kesannya terhadap prestasi akademik: tinjauan terhadap pelajar Sarjana Muda Kejuruteraan Mekanikal Sesi 1999/2000 KUiTTHO

Laporan Projek Sarjana ini mempersembahkan hasil kajian yang bertajuk 'PENGARUH MOTIVASI DAN KESANNYA TERHADAP PRESTASI AKADEMIK'. Kajian ini bertujuan untuk mengenalpasti hubungan faktor-faktor yang signifikan dalam penentuan prestasi akademik pelajar (faktor dalaman, luaran dan persekitaran) dengan prestasi akademik yang diukur melalui Purata Markah Keseluruhan atau CGPA. Sampel kajian adalah seramai 60 orang pelajar Saijana Muda Kejuruteraan Mekanikal sesi 1999/2000 KUiTTHO. Kajian adalah berbentuk tinjauan yang menggunakan sejenis instrumen kajian dalam mendapatkan data iaitu borang soal selidik. Kesemua data dianalisis dan dikemukakan dalam bentuk analisis statistik secara deskriptif dan secara inferensi. Korelasi Pearson digunakan untuk melihat hubungan antara setiap pembolehubah. Terdapat tiga faktor utama yang dikaji iaitu faktor dalaman(min=3.6), faktor luaran(min=3.7) dan faktor persekitaran (min=2.9). Hasil kajian menunjukkan bahawa ketiga-tiga tiga faktor tersebut mempunyai hubungan yang positif dengan prestasi akademik. Faktor dalaman yang paling memberi hubungan yang signifikan dalam prestasi akademik dengan 0.795, faktor luaran 0.650 dan faktor persekitaran 0. 339. Di akhir kajian ini, pengkaji mencadangkan agar (i) Mengadakan banyak Kem Motivasi, (ii) Peningkatan cara pengajaran pensyarah, (iii) Penyediaan peralatan pembelajaran yang mencukupi, (iv) Sumber rujukan seperti buku dan majalah di Perpustakaan mesti mencukupi

Computational Modelling of Spatio-Temporal EEG Brain Data with Spiking Neural Networks

The research presented in this thesis is aimed at modelling, classification and understanding of functional changes in brain activity that forewarn of the onset and/or the progression of a neurodegenerative process that may result in a number of disorders, including cognitive impairments, opiate addiction, Epilepsy and Alzheimer’s Disease. The study of neural plasticity and disease onset have been the centre of attention for researchers; especially as the population is ageing there is a need to deal with the increase in cognitive decline and the early onset of neurological diseases. As a consequence, large amounts of brain data has been collected and even more is expected to be collected, by means of novel computational techniques and biochemistry measurements. However, brain data is difficult to analyse and understand, especially since many of the traditional statistical and AI techniques are not able to deal with it appropriately. Driven by these issues and aiming to achieve the proposed goals, this study undertook to explore the potential of an evolving spatio-temporal data processing machine called the NeuCube architecture of spiking neurons, to analyse, classify and extract knowledge from electroencephalography spatio-temporal brain data. Firstly, the research undertaken in this thesis proposes a biologically plausible spiking neural network methodology for electroencephalography data classification and analysis. Secondly, it proposes a methodology for understanding functional changes in brain activity generated by the spatio-temporal data in the spiking neural network model. Thirdly, a new unsupervised learning rule is proposed for the investigation of the biological processes responsible for brain synaptic activity with the aim of targeting pharmacological treatments. The research undertaken achieved the following: high accuracy classification of electroencephalography data, even when fewer EEG channels and/or unprocessed data was used; personalised prognosis and early prediction of neurological events; the development of a tool for visualization and analysis of connectivity and spiking activity generated in the computational model; a better understanding of the impact of different drug doses on brain activity; a better understanding of specific neurological events by revealing the area of the brain where they occurred; and the analysis of the impact of biochemical processes on the neuronal synaptic plasticity of the model. Further improvement of the understanding and use of the proposed methodologies would contribute to the advancement of research in the area of prediction of neurological events and understanding of brain data related to neurological disorders, such as Alzheimer’s Disease

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment

The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging

Attention impairment in people with Parkinson– related Pisa Syndrome

Background and Aims: Pisa Syndrome (PS) is a disabling postural deformity, with a strong impact on life quality of people with Parkinson’s disease (pwPD). However, it can be reversible if diagnosed and treated at an early stage. Our purpose is to analyze PS from a different perspective: the ocular behavior, through the Eye Tracking methodology. Our goal is to shed light on the association between PS and the deficit of the visuospatial and attentive functions, as explored by the Eye Tracking approach, looking into clinical predictors of reduced visuospatial abilities and inferring on the need for adapting the rehabilitation approach. Methods: This cross sectional study, comparing behavior reactions and pattern of visual scanning in a group of pwPD – with (PS+) or without (PS-) trunk postural deviation - and a group of healthy age-matched people (HC), was held using the eye tracker EyeLink 1000. The Benton Judgment of Line Orientation Test was used to create a set of stimuli consecutively presented on the screen, while tracking patients’ gaze. Results: PS+ show a deficit of visuospatial functions, attention distribution and a reduced awareness of their posture. Neuropsychological features correlate with the pattern of visual scanning, which is significantly different in PS+ from PS- and HC. Attention impairment significantly impacts patients’ performance on the Benton test. Conclusions: Visuospatial ability impairment should be taken into account when planning the rehabilitation approach to postural abnormalities in pwP

acellular dermal matrix and heel reconstruction a new prospective

BackgroundHeel reconstruction represents a challenge for all plastic surgeons due to the anatomical and functional features of this weight-bearing area. In the last decade a combined use of acellul..

Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks

Publisher Copyright: © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.The genetic mechanisms responsible for the differentiation, metabolism, morphology and function of a cell in both normal and abnormal conditions can be uncovered by the analysis of transcriptomes. Mining big data such as the information encoded in nucleic acids, proteins, and metabolites has challenged researchers for several years now. Even though bioinformatics and system biology techniques have improved greatly and many improvements have been done in these fields of research, most of the processes that influence gene interaction over time are still unknown. In this study, we apply state-of-the art spiking neural network techniques to model, analyse and extract information about the regulatory processes of gene expression over time. A case study of microarray profiling in human skin during elicitation of eczema is used to examine the temporal association of genes involved in the inflammatory response, by means of a gene interaction network. Spiking neural network techniques are able to learn the interaction between genes using information encoded from the time-series gene expression data as spikes. The temporal interaction is learned, and the patterns of activity extracted and analysed with a gene interaction network. Results demonstrated that useful knowledge can be extracted from the data by using spiking neural network, unlocking some of the possible mechanisms involved in the regulatory process of gene expression.Several people have contributed to the research that resulted in this work, especially: Dr. Y. Chen, Dr. J. Hu, L. Zhou, Dr. E. Tu and Maryam Gholami-Doborjeh. Many thanks to Caitlin Veale and Kate Steckmest for proofreading the manuscript. Funding was provided by Auckland University of Technology, New Zealand (SRIF INTERACT 2017-18).Peer reviewe