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.

Characterisation of nickel(II) extraction by 2-hydroxy-5-nonylacetophenone oxime (LIX 84) in a micellar phase

The properties of the nickel(II)/2-hydroxy-5-nonylacetophenone oxime (HNAPO), an active ingredient in LIX 84, extraction system were characterised in a micellar system. The extinction coefficient, λ_max of HNAPO (316 nm) and the Ni²⁺ complex (387 nm) in a neutral micellar system, poly dispersed octa-ethyleneglycol mono-n-dodecyl ether (G₁₂A₈) were determined as 3100 and 3500 M⁻¹ cm⁻¹, respectively. HNAPO was found to have a neutral micellar phase and bulk aqueous phase pK_a of 11.5 and 12.5, respectively. The extraction equilibrium constant, K_ex, was determined to be 10^−8.0, and the deviation from theory observed at high pH can be accounted for by consideration of the competition for nickel(II) ions by hydroxide ions and HNAPO. A micellar phase of octa-ethyleneglycol mono-n-dodecyl ether (C₁₂E₈) was determined to be an appropriate model of the free oil/water interface from the solubilised location of HNAPO. Utilising the interfacial probe, 4-heptadecyl-7-hydroxy coumarin (HHC) allowed the determination of the electrostatic surface potential of mixed micelles of G₁₂A₈ and sodium dodecyl sulphate (SDS) or dodecyl trimethyl ammonium chloride (DTAC). The electrostatic surface potential was a linear function of the number of additional surfactant monomers within the G₁₂A₈ micelle, for the concentration range studied. For G₁₂A₈/DTAC mixed micelles, the surface potential was given by +1.1 mV per DTAC molecule per micelle, and for G₁₂A₈/SDS mixed micelles the relationship was −1.4 mV per SDS molecule per micelle.

Extraction of silver(i) from aqueous solutions in the absence and presence of copper(ii) with a methimazole-based ionic liquid

The ionic liquid (IL) 2-butylthiolonium bis(trifluoromethanesulfonyl)amide, [mimSBu][NTf₂], facilitates the efficient extraction of silver(i) from aqueous media via interaction with both the cation and anion components of the IL. Studies with a conventional aqueous-IL two phase system as well as microextraction of silver(i) by a thick IL film adhered to an electrode monitored in situ by cyclic voltammetry, established that [mimSBu][NTf₂] can extract electroactive silver(i) ions from an aqueous solution. The pH of the aqueous phase decreases upon addition of [mimSBu]⁺, which is attributed to partial release of the hydrogen attached to the N(3) nitrogen atom of the imidazolium ring. The presence of silver(i) further increase the acidity of the aqueous phase as a consequence of coordination with the IL cation component. Voltammetric and ¹H and ¹³C NMR techniques have been used to establish the nature of the silver(i) complexes extracted, and show that the form of interaction with the IL differs from that outlined previously for the extraction of copper(ii). Insights on the competition established when silver(i) is extracted in the presence of copper(ii) are provided. Finally, it is noted that metallic silver can be directly electrodeposited at the electrode surface after extraction of silver(i) into [mimSBu][NTf₂] and that back extraction of silver(i) into aqueous media is achieved by addition of an acidic aqueous solution.

Hotelling-downs competition with free entry when voters have an option to contest

Osborne shows that for almost all distributions of voters' preferences, a pure strategy Nash equilibrium does not exist in the classical Hotelling-Downs model of electoral competition with free entry. We show that equilibrium is generically possible if in addition one allows voters an option to announce their candidacy to compete side-by-side with office-seeking players. The model studied in this paper renders Osborne and the celebrated citizen-candidate model à la Osborne and Slivinski as two extreme cases. We characterize the equilibrium set with two central questions: (i) can there be equilibria where only voters contest? and (ii) are equilibria with contesting office-seeking players possible? We also show that in our general setting, extremists are typically voter-candidates so that in every two-party contest, office-seeking politicians stay out of competition.

Does inter-jurisdictional competition engender a “race to the bottom”? A meta-regression analysis

A growing literature documents the existence of strategic political reactions to publicexpenditure between rival jurisdictions. These interactions can potentially createa downward expenditure spiral (“race to the bottom”) or a rising expenditure spiral(“race to the top”). However, in the course of identifying the existence of such interactions and ascertaining their underlying triggers, the empirical evidence has produced markedly heterogeneous findings. Most of this heterogeneity can be traced back to study design and institutional differences. This article contributes to the literature by applying meta-regression analysis to quantify the magnitude of strategic inter-jurisdictional expenditure interactions, controlling for study, and institutional characteristics. We find several robust results beyond confirming that jurisdictions do engage in strategic expenditure interactions, namely that strategic interactions: (i) are weakening over time, (ii) are stronger among municipalities than among higher levels of government, and (iii) appear to be more influenced from tax competition than yardstick competition, with capital controls and fiscal decentralization shaping the magnitude of fiscal interactions.