Hai-Dang Nguyen

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This paper presents a real-time Electroencephalogram (EEG) classification system, with the goal of enhancing the control of a head-movement controlled power wheelchair for patients with chronic Spinal Cord Injury (SCI). Using a 32 channel recording device, mental command data was collected from 10 participants. This data was used to classify three different(More)
This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric(More)
Mobility has become very important for our quality of life. A loss of mobility due to an injury is usually accompanied by a loss of self-confidence. For many individuals, independent mobility is an important aspect of self-esteem. Head movement is a natural form of pointing and can be used to directly replace the joystick whilst still allowing for similar(More)
This paper describes a driver fatigue detection system using an artificial neural network (ANN). Using electroencephalogram (EEG) data sampled from 20 professional truck drivers and 35 non professional drivers, the time domain data are processed into alpha, beta, delta and theta bands and then presented to the neural network to detect the onset of driver(More)
This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer(More)
Loss of mobility can occur for a variety of reasons,such as spinal cord injury or motor neurone disease. The onset of these conditions often brings with it an associated loss of personal independence, which is primarily due to the fact that the sufferer is no longer able to control their mobility. This project aims to address this problem through the(More)
Driver fatigue is a prevalent problem and a major risk for road safety accounting for approximately 20-40% of all motor vehicle accidents. One strategy to prevent fatigue related accidents is through the use of countermeasure devices. Research on countermeasure devices has focused on methods that detect physiological changes from fatigue, with the fast(More)
Hypoglycaemia is a serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal) continuously to provide early detection of hypoglycemic episodes in Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, an evolved(More)
This paper presents a hands-free head-movement gesture classification system using a Neural Network employing the Magnified Gradient Function (MGF) algorithm. The MGF increases the rate of convergence by magnifying the first order derivative of the activation function, whilst guaranteeing convergence. The MGF is tested on able-bodied and disabled users to(More)
Brain computer interfaces (BCIs) can be implemented into assistive technologies to provide 'hands-free' control for the severely disabled. BCIs utilise voluntary changes in one's brain activity as a control mechanism to control devices in the person's immediate environment. Performance of BCIs could be adversely affected by negative physiological conditions(More)