Hidden conditional random fields for classification of imaginary motor tasks from EEG data

Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in BCIs is the imagination of motor activity, which produces variations on the power of the electroencephalography (EEG) signals recorded over the motor cortex. In this paper, we propose a new approach for classification of imaginary motor tasks based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they involve learned statistical models matched to the classification problem; they do not suffer from some of the limitations of generative models; and they include latent variables that can be used to model different brain states in the signal. Our approach involves auto-regressive modeling of the EEG signals, followed by the computation of the power spectrum. Frequency band selection is performed on the resulting time-frequency representation through feature selection methods. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV and the results show that our approach overperforms all methods proposed in the competition. In addition, we present a comparison with an HMM-based method, and observe that the proposed method produces better classification accuracy

Derivation of the spin Hamiltonians for Fe in MgO

A method to calculate the effective spin Hamiltonian for a transition metal impurity in a non- magnetic insulating host is presented and applied to the paradigmatic case of Fe in MgO. In a first step we calculate the electronic structure employing standard density functional theory (DFT), based on generalized-gradient approximation (GGA), using plane waves as a basis set. The corresponding basis of atomic-like maximally localized Wannier functions is derived and used to represent the DFT Hamiltonian, resulting in a tight-binding model for the atomic orbitals of the magnetic impurity. The third step is to solve, by exact numerical diagonalization, the N electron problem in the open shell of the magnetic atom, including both effect of spin-orbit and Coulomb repulsion. Finally, the low energy sector of this multi-electron Hamiltonian is mapped into effective spin models that, in addition to the spin matrices S, can also include the orbital angular momentum L when appropriate. We successfully apply the method to Fe in MgO, considering both, the undistorted and Jahn-Teller (JT) distorted cases. Implications for the influence of Fe impurities on the performance of magnetic tunnel junctions based on MgO are discussed.Comment: 10 pages, 7 Figure

Modeling differences in the time-frequency representation of EEG signals through HMM’s for classification of imaginary motor tasks

Brain Computer interfaces are systems that allow the control of external devices using the information extracted from the brain signals. Such systems find applications in rehabilitation, as an alternative communication channel and in multimedia applications for entertainment and gaming. In this work, a new approach based on the Time-Frequency (TF) distribution of the signal power, obtained by autoregressive methods and the use Hidden Markov models (HMM) is developed. This approach take into account the changes of power on different frequency bands with time. For that purpose HMM’s are used to modeling the changes in the power during the execution of two different motor tasks. The use of TF methods involves a problem related to the selection of the frequency bands that can lead to over fitting (due to the course of dimensionality) as well as problems related to the selection of the model parameters. These problems are solved in this work by combining two methods for feature selection: Fisher Score and Sequential Floating Forward Selection. The results are compared to the three top results of the BCI competition IV. It is shown here that the proposed method over perform those other methods in four subjects and the average over all the subjects equals the one obtained by the winner algorithm of the competition

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on auto-regressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy

Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data

In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy

Effect of cytostatic drugs on microbial behaviour in membrane bioreactor system

The aim of this work is to evaluate the influence of cyclophosphamide and its principal metabolites (CPs) on microbial behaviour in a membrane bioreactor system. Two laboratory-scale membrane bioreactors (MBR) were run in parallel with a sludge retention time of 70 days (one with the cytostatic drugs, MBR-CPs, the second without, MBR-control). The microbial activity was measured by respirometric analysis. The endogenous and exogenous respirations of heterotrophic micro-organisms were evaluated. Micro-organisms exposed to CPs showed higher endogenous respiration rates and lower exogenous respiration rates than micro-organisms present in MBR-control. The effects were observed several days after adding the cocktail. Reduced sludge production was observed in MBR-CPs compared to MBR-control. This reduction of sludge production and the increase in the endogenous respiration rate in relation to MBR-control suggest that the chemical stress caused by CPs led to a diversion of carbon and/or energy from growth to adaptive responses and protection. In addition, the inhibitory effect on the assimilation of exogenous substrate (reduced exogenous respiration rate) suggests an inhibition of catabolism and anabolism despite the low CPs concentration studied (μg/L). However, this inhibitory effect can be offset by the biomass still active under low ratio (substrate/biomass) conditions in the bioreactor (due to complete retention of biomass and high sludge age), which helped to maintain high overall performance in the removal of conventional pollution

Nonlinear switched-current CMOS IC for random signal generation

A nonlinear switched-current circuit is presented that implements a chaotic algorithm for the generation of broadband, white analogue noise. The circuit has been fabricated in a double-metal, single-poly 1.6µm CMOS technology and uses a novel, highly accurate CMOS circuit strategy to realise piecewise-linear characteristics in the current-mode domain. Measurements from the silicon prototype show a flat spectrum from DC to ~30% of the clock frequency, for a clock frequency of 500kHz

La laïcitat a l'Escola

Abstract not availabl

A variable in Paulo Mendes da Rocha’s single-storey houses

At the age of twenty-nine years old, the young Paulo Mendes da Rocha had already designed notable projects, and it was at that age, in 1958, when his work became widely recognised by winning the competition for the Paulistano Athletic Club Gymnasium with a project that soon became a reference in the national architecture context. This moment of maturation in his work corresponds to a period of significant events at the international level. Just two years before, events at CIAM 10 suggested that the generational tension pointed out by Le Corbusier led to the advent of numerous new perspectives affecting international architecture production. In parallel, some authors have already noticed subtle variations in Mendes da Rocha’s work that appeared in the 1970s and share ideas that had arisen in the new international context. Revisiting the twenty-one built and unbuilt single-storey house projects designed by the architect (all of which were designed between 1961 and 2012), by analysing the relationship between the interior ‘public’ and ‘private’ spaces, it is possible to identify variations that mirror shifts at the international level. Noting that there is a divergence of solutions proposed by Mendes da Rocha in his first houses when compared to his latest designs, this paper joins recent contributions of other authors showing heterogeneities in the architect’s work and showing possible new directions in his work that appeared during the post-CIAM years.info:eu-repo/semantics/publishedVersio