Biomedical subjects

Gary McDarby

Publications and source records attributed to Gary McDarby.

3 recordsLinked to original sources

PubMed2006

The VESPA: a method for the rapid estimation of a visual evoked potential.

Faster and less obtrusive means for measuring a Visual Evoked Potential would be valuable in clinical testing and basic neuroscience research. This study presents a method for accomplishing this by smoothly modulating the luminance of a visual stimulus using a stochastic process. Despite its visually unobtrusive nature, the rich statistical structure of the stimulus enables rapid estimation of the visual system's impulse response. The profile of these responses, which we call VESPAs, correlates with standard VEPs, with r=0.91, p<10(-28) for the group average. The time taken to obtain a VESPA with a given signal-to-noise ratio compares favorably to that required to obtain a VEP with a similar level of certainty. Additionally, we show that VESPA responses to two independent stimuli can be obtained simultaneously, which could drastically reduce the time required to collect responses to multiple stimuli. The new method appears to provide a useful alternative to standard VEP methods, and to have potential application both in clinical practice and to the study of sensory and perceptual functions.

Adult↗

PubMed2005

Visual spatial attention control in an independent brain-computer interface.

This paper presents a novel brain computer interface (BCI) design employing visual evoked potential (VEP) modulations in a paradigm involving no dependency on peripheral muscles or nerves. The system utilizes electrophysiological correlates of visual spatial attention mechanisms, the self-regulation of which is naturally developed through continuous application in everyday life. An interface involving real-time biofeedback is described, demonstrating reduced training time in comparison to existing BCIs based on self-regulation paradigms. Subjects were cued to covertly attend to a sequence of letters superimposed on a flicker stimulus in one visual field while ignoring a similar stimulus of a different flicker frequency in the opposite visual field. Classification of left/right spatial attention is achieved by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli. Six out of eleven physically and neurologically healthy subjects demonstrate reliable control in binary decision-making, achieving at least 75% correct selections in at least one of only five sessions, each of approximately 12-min duration. The highest-performing subject achieved over 90% correct selections in each of four sessions. This independent BCI may provide a new method of real-time interaction for those with little or no peripheral control, with the added advantage of requiring only brief training.

Adult↗

PubMed2004

On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces.

A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user. Ultimately this can lead to abandonment of the device. The primary reason for this is that relatively indirect measures of cognitive function, as can be gleaned from the electroencephalogram (EEG), are harnessed. A more suitable and usable interface would need to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. In this paper, we propose a novel approach, using non-invasive near-infrared imaging technology to develop a user-friendly optical BCI. As an alternative to the traditional EEG-based devices, we have used practical non-invasive optical techniques to detect characteristic haemodynamic responses due to motor imagery and consequently created an accessible BCI that is simple to attach and requires little user training.

Algorithms↗