Neural field models describe the coarse-grained activity of populations of interacting neurons. Because of the laminar structure of real cortical tissue they are often studied in two spatial dimensions, where they are well known to generate rich patterns of spatiotemporal activity. Such patterns have been interpreted in a variety of contexts ranging from the understanding of visual hallucinatio...

This paper describes the pattern recognition techniques used to develop a robotic interface utilizing neural signals recorded from the primary motor cortex area of the brain. The ultimate objective of this research is to provide severely disabled individuals with a natural method of controlling prosthetic devices. The short term goal of this project is to develop a natural neural signal interfa...

Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a...

Artificial Neural Networks are bio-inspired mechanisms for intelligent decision support. Artificial Neural Networks have got remarkable ability to learn and derive meaning from large amount of domain data. The paper discusses the limitations of ANN from user interface perspective and emphasizes how fuzzy logic can serve as an effective user interface tool for ANN. The paper presents a framework...

In this paper, a new Discontinuity Capturing Shallow Neural Network (DCSNN) for approximating $d$-dimensional piecewise continuous functions and solving elliptic interface problems is developed. There are three novel features in the present network; namely, (i) jump discontinuities accurately captured, (ii) it completely shallow, comprising only one hidden layer, (iii) mesh-free partial differe...

This paper presents a "direct" method to gas-oil interface level determination using an artificial neural network approach based on Electrical Capacitance Tomography (ECT) measurements. "Direct" here means that the gas-oil interface levels are obtained directly from the ECT measurements without recourse to image reconstruction. The preliminary work models a separation tank that i~ filled with g...