A method for classifying mental tasks in the space of EEG transforms

In this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assigns class-dependent information weights to individual pixels, so that the decision is defined by the summary weights of the most informative pixel features. We experimentally analyze several versions of the approach. The informative features appear to be rather similar among different individuals, thus supporting the view that there are subject independent general brain patterns for the same mental task

Incorporation of the statistical uncertainty in the background estimate into the upper limit on the signal

We present a procedure for calculating an upper limit on the number of signal events which incorporates the Poisson uncertainty in the background, estimated from control regions of one or two dimensions. For small number of signal events, the upper limit obtained is more stringent than that extracted without including the Poisson uncertainty. This trend continues until the number of background events is comparable with the signal. When the number of background events is comparable or larger than the signal, the upper limit obtained is less stringent than that extracted without including the Poisson uncertainty. It is therefore important to incorporate the Poisson uncertainty into the upper limit; otherwise the upper limit obtained could be too stringent.Comment: 14 pages, 4 figure

Wavelet design by means of multi-objective GAs for motor imagery EEG analysis

Wavelet-based analysis has been broadly used in the study of brain-computer interfaces (BCI), but in most cases these wavelet functions have not been designed taking into account the requirements of this field. In this study we propose a method to automatically generate wavelet-like functions by means of genetic algorithms. Results strongly indicate that it is possible to generate (evolve) wavelet functions that improve the classification accuracy compared to other well-known wavelets (e.g. Daubechies and Coiflets)

Distributed simulation and industry: Potentials and pitfalls

We present the views of five researchers and practitioners of distributed simulation. Collectively we attempt to address what the implications of distributed simulation are for industry. It is hoped that the views contained herein, and the presentations made by the panelists at the 2002 Winter Simulation Conference will raise awareness and stimulate further discussion on the application of distributed simulation methods and technology in an area that is yet to benefit from the arguable economic benefits that this technique promises

Secure Full-Duplex Two-Way Relaying for SWIPT

This letter studies bi-directional secure information exchange in a simultaneous wireless information and power transfer (SWIPT) system enabled by a full-duplex (FD) multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay. The AF relay injects artificial noise (AN) in order to confuse the eavesdropper. Specifically, we assume a zeroforcing (ZF) solution constraint to eliminate the residual self-interference (RSI). As a consequence, we address the optimal joint design of the ZF matrix and the AN covariance matrix at the relay node as well as the transmit power at the sources. We propose an alternating algorithm utilizing semi-definite programming (SDP) technique and one-dimensional searching to achieve the optimal solution. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.Comment: Submitted to IEEE Wireless Communications Letter