Timothy Cutmore

Cognitive and gender factors influencing navigation in a virtual environment

Virtual environments (VEs) are becoming popular as media for training, modelling and entertainment. Little is known, however, about the factors that affect efficient and rapid acquisition of knowledge using this technology. Five experiments examined the influence of gender, passive/active navigation, cognitive style, hemispheric activation measured by electroencephalography and display information on the acquisition of two types of navigational knowledge using a VE: route and survey knowledge. Males acquired route knowledge from landmarks faster than females. In situations where survey knowledge must be used, proficiency in visual-spatial cognition is associated with better performance. The right cerebral hemisphere appears to be more activated than the left during navigational learning in a VE. In identifying cognitive factors that influence VE navigation, these results have a number of implications in the use of VEs for training purposes and may assist in linking processes involved in navigation to a more general framework of visual-spatial processing and mental imagery.

Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising.

OBJECTIVES The aim of this paper is to introduce and test a general, wavelet-based method for the automatic removal of noise and artefact from psychophysiological data. METHODS Statistical wavelet thresholding (SWT) performs blind source separation by transforming data to the wavelet domain, and subsequent filtering of wavelet coefficients based on a statistical framework. The observed wavelet coefficients are modelled using a Gaussian distribution, from which low-probability outliers are attenuated based on their z-scores. RESULTS The technique was applied to both simulated and real event-related potentials (ERP) data. SWT applied to artificial data displayed increased signal-to-noise ratio (SNR) improvements as noise amplitude increased. ERP averages of filtered experimental data displayed a correlation of 0.93 with operator-filtered data, compared with a correlation of 0.56 for unfiltered data. The energy of operator-designated contaminated trials was attenuated by a factor of 7.46 relative to uncontaminated trials. SNR improvement was observed in simulated tests. CONCLUSIONS Variations of SWT may be useful in situations where one wishes to separate uncommon/uncharacteristic structures from time series data sets. For artefact removal applications, SWT appears to be a valid alternative to expert operator screening.

Wavelet coherence of EEG signals for a visual oddball task

Neural co-activation in frontal and central cortex was examined during a visual oddball task using wavelet coherence. EEG was recorded during a visual oddball task, presented to 12 participants with a random mix of 15% oddball targets and 85% frequent non-target letters over 265 trials. Wavelet coherence of individual trials was shown to distinguish frequent and oddball trials. Averaged wavelet coherence showed significant differences: oddball targets showed higher delta-theta activity whereas frequent background stimuli showed higher gamma activity. Increased gamma coherence appeared to be related to expectation of the targets with our analysis showing an R(2) of 0.935 for the relationship between averaged sections of gamma coherence and the number of intervening (frequent) trials since the last oddball.

Sensors and Sensor Systems for Psychophysiological Monitoring: A Review of Current Trends

Abstract. There are a wide range of sensors for acquiring signals from the human body in noninvasive ways. Some of those in use date back a few decades, and many new technologies have enabled different sensors designs in recent years. This review covers the following classes of sensors: electric, magnetic, electrochemical, mechanical, thermal, and optical. Sensor systems that are portable, safe, and low-cost are now becoming possible. This review provides an overview focussing on the technology behind sensors currently used by psychophysiologists with the objective of illuminating the choices available for acquiring signals that inform us about cognitive processes, emotional states, and behavioural patterns. In previously published encyclopaedic-type reviews of psychophysiology, the focus has been on what is measured, not how it is measured. By focussing on how the sensors and sensor systems work, this review aims to provide users of these technologies with information that will help them decide on the ap...

Automated eye tracking system calibration using artificial neural networks

The electro-oculogram (EOG) continues to be widely used to record eye movements especially in clinical settings. However, an efficient and accurate means of converting these recordings into eye position is lacking. An artificial neural network (ANN) that maps two-dimensional (2D) eye movement recordings into 2D eye positions can enhance the utility of such recordings. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33 degrees . Linear perceptrons (LPs) were only nearly half as accurate. For five subjects, the mean accuracy provided by the MLPs was 1.09 degrees of visual angle ( degrees ) for EOG data, and 0.98 degrees for an infrared eye tracker. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different from that obtained with the infrared tracker. Using initial weights trained on another person reduced MLP training time, reaching convergence in as little as 20 iterations.

Automated ECG diagnostic P-wave analysis using wavelets

P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous wavelet transform (CWT) which are shown to be potentially effective discriminators in an automated diagnostic process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These wavelet models were also compared to standard cardiological measures of duration, terminal force and duration divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the present study.

Noise Reduction in Rhythmic and Multitrial Biosignals With Applications to Event-Related Potentials

A new noise reduction algorithm is presented for signals displaying repeated patterns or multiple trials. Each pattern is stored in a matrix, forming a set of events, which is termed multievent signal. Each event is considered as an affine transform of a basic template signal that allows for time scaling and shifting. Wavelet transforms, decimated and undecimated, are applied to each event. Noise reduction on the set of coefficients of the transformed events is applied using either wavelet de- noising or principal component analysis (PCA) noise reduction methodologies. The method does not require any manual selection of coefficients. Nonstationary multievent synthetic signals are employed to demonstrate the performance of the method using normalized mean square error against classical wavelet and PCA based algorithms. The new method shows a significant improvement in low SNRs (typically <0 dB). On the experimental side, evoked potentials in a visual oddball paradigm are used. The reduced-noise visual oddball event-related potentials reveal gradual changes in morphology from trial to trial (especially for N1-P2 and N2-P3 waves at Fz), which can be hypothetically linked to attention or decision processes. The new noise reduction method is, thus, shown to be particularly suited for recovering single-event features in non- stationary low SNR multievent contexts.

FPGA implementation of Morlet continuous wavelet transform for EEG analysis

This article presents the design and implementation of continuous wavelet transform (CWT) of nonstationary Electroencephalogram (EEG) signals using a Spartan 3AN FPGA. The widely applied Morlet wavelet function was used for obtaining the CWT coefficients. The complex convolutions were executed in Fourier space using simple multipliers. Altium designer was used to import Xilinx FFT core configured in Radix 4. Two VHDL controllers were built to control the FFT core operation (which handles both the FFT-IFFT computations) for the first controller; and the multiplication processes in between for the second one. The results showed that the digital architecture of Morlet wavelet function in Fourier space is very time efficient. By an optimized trade-off between speed and silicon area, the design can produce the wavelet coefficients at all scales of 1024 points EEG signal in approximately 1 msec when it runs at maximum clock speed of 125 MHz.

Internet based ECG medical information system

Physiological monitoring of humans for medical applications is well established and ready to be adapted to the Internet. This paper describes the implementation of a Medical Information System (MIS-ECG system) incorporating an Internet based ECG acquisition device. Traditionally clinical monitoring of ECG is largely a labour intensive process with data being typically stored on paper. Until recently, ECG monitoring applications have also been constrained somewhat by the size of the equipment required. Today’s technology enables large and fixed hospital monitoring systems to be replaced by small portable devices. With an increasing emphasis on health management a truly integrated information system for the acquisition, analysis, patient particulars and archiving is now a realistic possibility. This paper describes recent Internet and technological advances and presents the design and testing of the MIS-ECG system that utilises these advances.

Effects of cue frequency and repetition on prospective memory: An ERP investigation

Prospective memory involves the formation and completion of delayed intentions and is essential for independent living. In this study (n = 33), event-related potentials (ERPs) were used to systematically evaluate the effects of PM cue frequency (10% versus 30%) and PM cue repetition (high versus low) on ERP modulations. PM cues elicited prospective positivity and frontal positivity but not N300, perhaps due to the semantic nature of the task. Results of this study revealed an interesting interaction between PM cue frequency and PM cue repetition for prospective positivity and frontal positivity, highlighting the importance of taking both factors into account when designing future studies.