EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

EEG signal analysis for BCI application using fuzzy system

An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.

EEG data classification using wavelet features selected by Wilcoxon statistics

This paper introduces a method to classify EEG signals using features extracted by an integration of wavelet transform and the nonparametric Wilcoxon test. Orthogonal Haar wavelet coefficients are ranked based on the Wilcoxon test’s statistics. The most prominent discriminant wavelets are assembled to form a feature set that serves as inputs to the naïve Bayes classifier. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed combination of Haar wavelet features and naïve Bayes classifier considerably dominates the competitive classification approaches and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II. Application of naïve Bayes also provides a low computational cost approach that promotes the implementation of a potential real-time BCI system.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

A tendência à queda da taxa de lucro: determinantes teóricos e a economia brasileira

Talvez seja excesso de pessimismo crer que se inicia a derrocada final do capitalismo. O fato, porém, é que esse sistema vem se defrontando com problemas para os quais não há ainda soluções visíveis, como a questão do emprego, por exemplo. As taxas mundiais de desemprego são elevadas e a globalização da economia tende a reforçar o desemprego estrutural. Daí a preocupação do G-7, que tem analisado o tema recorrentemente. O objetivo do presente trabalho, consiste na análise do comportamento apresentado pela taxa de lucro. Para tanto, ela será pensada de acordo com a interpretação marxista. Isto envolve uma análise teórica e empírica. Qualquer tentativa de estimação de conceitos marxistas, porém, deve abordar a questão da transformação de valores em preços.

Investment coordination failures and the confidence channel of fiscal policy

Este trabalho desenvolve um novo "canal de Confiança" da política fiscal e caracteriza a política ótima quando esse canal é levado em consideração. Para esse objetivo, utilizamos um modelo estático com (i) concorrência monopolística, (ii) custos de ajustamento fixos para investir, (iii) complementaridade estratégica devido a informação imperfeita com respeito a produtividade agregada, e (iv) bens privados como substitutos imperfeitos de bens privados. Este arcabouço acomoda a possibilidade de falhas de coordenação nos investimentos, mas apresenta um equilíbrio único. Mostramos que a política fiscal tem efeitos importantes na coordenação. Um aumento dos gastos do governo leva a uma maior demanda por bens privados. Mais importante, este também afeta as expectativas de ordem superior com relação a demanda das demais firmas, que amplifica os efeitos do aumento inicial da demanda devido a complementaridade estratégica nas decisões de investimento. Como as demais firmas estão se deparam com uma demanda maior, espera-se que estas invistam mais, que por sua vez, aumenta a demanda individual de cada firma, que aumenta os incentivos a investir. Denominamos isto como o "canal de confiança" da política fiscal. Sob a ameaça de falhas de coordenação, a política fiscal ótima prescreve produzir além do ponto em que o benefício marginal resultante do consumo de bens públicos é igual ao custo marginal desses bens. Este benefício adicional vem do fato de que a política fiscal pode ampliar a coordenação dos investimentos.

Study of (Ba3MMWO9)-M-II-W-IV (M-II = Ca, Zn; M-IV = Ti, Zr) perovskite oxides: Competition between 3C and 6H perovskite structures

We describe an investigation of (Ba3MMWO9)-M-II-W-IV oxides for M-II = Ca, Zn, and other divalent metals and M-IV = Ti, Zr. In general, a 1:2-ordered 6H (hexagonal, P6(3)/mmc) perovskite structure is stabilized at high temperatures (1300 degrees C) for all of the (Ba3MTiWO9)-Ti-II oxides investigated. An intermediate phase possessing a partially ordered 1:1 double perovskite (3C) structure with the cation distribution, Ba-2(Zn2/3Ti1/3)(W2/3Ti1/3)O-6, is obtained at 1200 degrees C for Ba3ZnTiWO9. Sr substitution for Ba in the latter stabilizes the cubic 3C structure instead of the 6H structure. A metastable Ba3CaZrWO9 that adopts the 3C (cubic, Fm (3) over barm) structure has also been synthesized by a low-temperature metathesis route. Besides yielding several new perovskite oxides that may be useful as dielectric ceramics, the present investigation provides new insights into the complex interplay of crystal chemistry (tolerance factor) and chemical bonding (anion polarization and d(0)-induced distortion of metal-oxygen octahedra) in the stabilization of 6H versus 3C perovskite structures for the (Ba3MMWO9)-M-II-W-IV series.

Study of (Ba3MMWO9)-M-II-W-IV (M-II = Ca, Zn; M-IV = Ti, Zr) perovskite oxides: Competition between 3C and 6H perovskite structures

We describe an investigation of (Ba3MMWO9)-M-II-W-IV oxides for M-II = Ca, Zn, and other divalent metals and M-IV = Ti, Zr. In general, a 1:2-ordered 6H (hexagonal, P6(3)/mmc) perovskite structure is stabilized at high temperatures (1300 degrees C) for all of the (Ba3MTiWO9)-Ti-II oxides investigated. An intermediate phase possessing a partially ordered 1:1 double perovskite (3C) structure with the cation distribution, Ba-2(Zn2/3Ti1/3)(W2/3Ti1/3)O-6, is obtained at 1200 degrees C for Ba3ZnTiWO9. Sr substitution for Ba in the latter stabilizes the cubic 3C structure instead of the 6H structure. A metastable Ba3CaZrWO9 that adopts the 3C (cubic, Fm (3) over barm) structure has also been synthesized by a low-temperature metathesis route. Besides yielding several new perovskite oxides that may be useful as dielectric ceramics, the present investigation provides new insights into the complex interplay of crystal chemistry (tolerance factor) and chemical bonding (anion polarization and d(0)-induced distortion of metal-oxygen octahedra) in the stabilization of 6H versus 3C perovskite structures for the (Ba3MMWO9)-M-II-W-IV series.

Effect of the competition of Cu(II) and Ni(II) on the kinetic and thermodynamic stabilities of Cr(III)-organic ligand complexes using competitive ligand exchange (EDTA)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Impact of automotive fuel economy standards on competition in the automotive industry. Volume II - technical report. Final report.

National Highway Traffic Safety Administration, Washington, D.C.