19,841 research outputs found

    Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

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    Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

    Profiling Tourists for Balanced Utilization of Tourism-Based Resources in Kenya

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    Kenya is predominantly a nature-based tourism destination with wildlife (concentrated in the southern part of the country) and beaches (along the Indian Ocean) accounting for over 85% of the international tourists visiting the country. Other attractions are based on the physical landscape of the country and the culture of the people. Unfortunately, the full potential of culture-based attractions has not been exploited. The over-concentration of tourism activities in wildlife protected areas and on the coastal zone has had inherent problems that include severe environmental degradation. The less visited attractions stand the risk of neglect and could be eroded from the nation’s heritage with time. There is need to diversify tourism activities and spread them to other parts of the country by putting more emphasis on non-traditional ones such as cultural excursions. This research profiles tourists based on their preferences as assessed from the number of days they spend at different attraction sites. By associating the characteristics of tourists with various attractions, consumer preference profiles were established. Length of stay, presence of children, travel party size and gender are some of the significant factors that determined the profiles. Profiles can be used in encouraging proportionately more tourists with greater affinity for non-traditional attractions. Besides gender, other factors such as socio-economic status and whether one is travelling as a couple or not, turned out to be significant variables in influencing the resulting expenditure levels.Tourist profiles, Attractions, Culture, Expenditure, LISREL, Kenya

    Asymptotic power of sphericity tests for high-dimensional data

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    This paper studies the asymptotic power of tests of sphericity against perturbations in a single unknown direction as both the dimensionality of the data and the number of observations go to infinity. We establish the convergence, under the null hypothesis and contiguous alternatives, of the log ratio of the joint densities of the sample covariance eigenvalues to a Gaussian process indexed by the norm of the perturbation. When the perturbation norm is larger than the phase transition threshold studied in Baik, Ben Arous and Peche [Ann. Probab. 33 (2005) 1643-1697] the limiting process is degenerate, and discrimination between the null and the alternative is asymptotically certain. When the norm is below the threshold, the limiting process is nondegenerate, and the joint eigenvalue densities under the null and alternative hypotheses are mutually contiguous. Using the asymptotic theory of statistical experiments, we obtain asymptotic power envelopes and derive the asymptotic power for various sphericity tests in the contiguity region. In particular, we show that the asymptotic power of the Tracy-Widom-type tests is trivial (i.e., equals the asymptotic size), whereas that of the eigenvalue-based likelihood ratio test is strictly larger than the size, and close to the power envelope.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1100 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org
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