Model based analysis of fMRI/EEG data

This thesis addresses two major topics in neuroscience literature and drawbacks from existing literature are addressed by utilising state space models and Bayesian estimation techniques. Particle filter-based joint estimation of the physiological model for time-series analysis of fMRI data is demonstrated first in the thesis and secondly the Granger causality-based effective connectivity analysis of EEG data is investigated

Passive muscle force analysis during vehicle access: a gender comparison

Ensuring customer satisfaction within the automotive industry is a top priority. Primary concerns of satisfaction revolve around perceived comfort of entering and exiting vehicles. The ease of this task is attributed mostly to the design of the vehicles door frame however these are not tailored towards a specific gender. In this paper we present a biomechanical analysis-based gender assessment during entering and exiting a vehicle. The proposed method of analysis provides an assessment that can be used to predict differences between genders. The trials conducted in this study used ten subjects entering a common family vehicle. The discomfort measure based on the normalised muscle forces relies on biomechanical analysis of posture sequences entering and exiting the vehicles

A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data

The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain\u27s electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.<br /

Parameter estimation of the BOLD fMRI model within a general particle filter framework

This work demonstrates a novel Bayesian learning approach for model based analysis of Functional Magnetic Resonance (fMRI) data. We use a physiologically inspired hemodynamic model and investigate a method to simultaneously infer the neural activity together with hidden state and the physiological parameter of the model. This joint estimation problem is still an open topic. In our work we use a Particle Filter accompanied with a kernel smoothing approach to address this problem within a general filtering framework. Simulation results show that the proposed method is a consistent approach and has a good potential to be enhanced for further fMRI data analysis

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem

Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities

BACKGROUND: Optical measurement techniques and recent advances in wearable technology have made heart rate (HR) sensing simpler and more affordable. OBJECTIVES: The Polar OH1 is an arm worn optical heart rate monitor. The objectives of this study are two-fold; 1) to validate the OH1 optical HR sensor with the gold standard of HR measurement, electrocardiography (ECG), over a range of moderate to high intensity physical activities, 2) to validate wearing the OH1 at the temple as an alternative location to its recommended wearing location around the forearm and upper arm. METHODS: Twenty-four individuals participated in a physical exercise protocol, by walking on a treadmill and riding a stationary spin bike at different speeds while the criterion measure, ECG and Polar OH1 HR were recorded simultaneously at three different body locations; forearm, upper arm and the temple. Time synchronised HR data points were compared using Bland-Altman analyses and intraclass correlation. RESULTS: The intraclass correlation between the ECG and Polar OH1, for the aggregated data, was 0.99 and the estimated mean bias ranged 0.27-0.33 bpm for the sensor locations. The three sensors exhibited a 95% limit of agreement (LoA: forearm 5.22, -4.68 bpm; upper arm 5.15, -4.49; temple 5.22, -4.66). The mean of the ECG HR for the aggregated data was 112.15 &plusmn; 24.52 bpm. The intraclass correlation of HR values below and above this mean were 0.98 and 0.99 respectively. The reported mean bias ranged 0.38-0.47 bpm (95% LoA: forearm 6.14, -5.38 bpm; upper arm 6.07, -5.13 bpm; temple 6.09, -5.31 bpm), and 0.15-0.16 bpm (95% LoA: forearm 3.99, -3.69 bpm; upper arm 3.90, -3.58 bpm; temple 4.06, -3.76 bpm) respectively. During different exercise intensities, the intraclass correlation ranged 0.95-0.99 for the three sensor locations. During the entire protocol, the estimated mean bias was in the range -0.15-0.55 bpm, 0.01-0.53 bpm and -0.37-0.48 bpm, for the forearm, upper arm and temple locations respectively. The corresponding upper limits of 95% LoA were 3.22-7.03 bpm, 3.25-6.82 bpm and 3.18-7.04 bpm while the lower limits of 95% LoA were -6.36-(-2.35) bpm, -6.46-(-2.30) bpm and -7.42-(-2.41) bpm. CONCLUSION: Polar OH1 demonstrates high level of agreement with the criterion measure ECG HR, thus can be used as a valid measure of HR in lab and field settings during moderate and high intensity physical activities