Use of an auditory looming task to test infants' sensitivity to sound pressure level as an auditory distance cue

This study describes how an auditory looming technique was used to investigate 4-to 6-month-old infants' sensitivity to sound pressure level (SPL) as an auditory distance cue. Thirty-two infants were tested in complete darkness and presented with auditory stimuli that underwent unidirectional variations in SPL (40–70dB). The rate at which SPL was varied during the course of trials (past vs. slow) was manipulated by varying trial length (5s vs. 10s).

Auditory localisation reversals and the role of torso-related cues

Human auditory localisation reversals are explored using mixture distribution analysis techniques. This is validated for front/back reversals and subsequently shown to provide evidence for up/down reversals as distinct classes of mis-localisation. Torso-related localisation cues are identified and also shown to provide a source for resolving these reversals in some listeners.

User-centered design and evaluation of an interactive visual-haptic-auditory interface : a user study on assembly

Utilizing user-centred system design and evaluation method has become an increasingly important tool to foster better usability in the field of virtual environments (VEs). In recent years, although it is still the norm that designers and developers are concerning the technological advancement and striving for designing impressive multimodal multisensory interfaces, more and more awareness are aroused among the development team that in order to produce usable and useful interfaces, it is essential to have users in mind during design and validate a new design from users' perspective. In this paper, we describe a user study carried out to validate a newly developed haptically enabled virtual training system. By taking consideration of the complexity of individual differences on human performance, adoption and acceptance of haptic and audio-visual I/O devices, we address how well users learn, perform, adapt to and perceive object assembly training. We also explore user experience and interaction with the system, and discuss how multisensory feedback affects user performance, perception and acceptance. At last, we discuss how to better design VEs that enhance users perception, their interaction and motor activity.

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's 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.

An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network

Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.

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.

EEG data classification using wavelet features selected by Wilcoxon statistics

This paper introduces a method to classify EEG signals using features extracted by an integration of wavelet transform and the nonparametric Wilcoxon test. Orthogonal Haar wavelet coefficients are ranked based on the Wilcoxon test’s statistics. The most prominent discriminant wavelets are assembled to form a feature set that serves as inputs to the naïve Bayes classifier. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed combination of Haar wavelet features and naïve Bayes classifier considerably dominates the competitive classification approaches and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II. Application of naïve Bayes also provides a low computational cost approach that promotes the implementation of a potential real-time BCI system.

No evidence for mirror system dysfunction in schizophrenia from a multimodal TMS/EEG study

Dysfunctional mirror neuron systems have been proposed to contribute to the social cognitive deficits observed in schizophrenia. A few studies have explored mirror systems in schizophrenia using various techniques such as TMS (levels of motor resonance) or EEG (levels of mu suppression), with mixed results. This study aimed to use a novel multimodal approach (i.e. concurrent TMS and EEG) to further investigate mirror systems and social cognition in schizophrenia. Nineteen individuals with schizophrenia or schizoaffective disorder and 19 healthy controls participated. Single-pulse TMS was applied to M1 during the observation of hand movements designed to elicit mirror system activity. Single EEG electrodes (C3, CZ, C4) recorded brain activity. Participants also completed facial affect recognition and theory of mind tasks. The schizophrenia group showed significant deficits in facial affect recognition and higher level theory of mind compared to healthy controls. A significant positive relationship was revealed between mu suppression and motor resonance for the overall sample, indicating concurrent validity of these measures. Levels of mu suppression and motor resonance were not significantly different between groups. These findings indicate that in stable outpatients with schizophrenia, mirror system functioning is intact, and therefore their social cognitive difficulties may be caused by alternative pathophysiology.