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

A study on temporal segmentation strategies for extracting common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) create a new approach to human computer communication, allowing the user to control a system simply by performing mental tasks such as motor imagery. This paper proposes and analyses different strategies for time segmentation in extracting common spatial patterns of the brain signals associated to these tasks leading to an improvement of BCI performance

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)

EVALUATING NATURE-BASED TOURISM USING THE NEW ENVIRONMENTAL PARADIGM

Nature-based tourism (NBT), alternatively known as ecotourism, is a rapidly expanding area in the tourism travel sector. States such as Louisiana with a well established urban-based tourism industry may have expansion opportunities through development of complementary nature-based tourism. This study analyzes the decision to participate in NBT among Louisiana tourists.Nature tourism, Ecotourism, NEP, Resource /Energy Economics and Policy,