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Exploring Cortical Attentional System by Using fMRI during a Continuous Perfomance TestEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Functional magnetic resonance imaging (fMRI) was performed in eight healthy subjects to identify the localization, magnitude, and volume extent of activation in brain regions that are involved in blood oxygen level-dependent (BOLD) response during the performance of Conners' Continuous Performance Test (CPT). An extensive brain network was activated during the task including frontal, temporal, and occipital cortical areas and left cerebellum. The more activated cluster in terms of volume extent and magnitude was located in the right anterior cingulate cortex (ACC). Analyzing the dynamic trend of the activation in the i...
Source: Computational Intelligence and Neuroscience - November 16, 2009 Category: Neuroscience Source Type: journals

State-Space Algorithms for Estimating Spike Rate FunctionsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic s...
Source: Computational Intelligence and Neuroscience - November 5, 2009 Category: Neuroscience Source Type: journals

Crossmodal Links between Vision and Touch in Spatial Attention: A Computational Modelling StudyEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Many studies have revealed that attention operates across different sensory modalities, to facilitate the selection of relevant information in the multimodal situations of every-day life. Cross-modal links have been observed either when attention is directed voluntarily (endogenous) or involuntarily (exogenous). The neural basis of cross-modal attention presents a significant challenge to cognitive neuroscience. Here, we used a neural network model to elucidate the neural correlates of visual-tactile interactions in exogenous and endogenous attention. The model includes two unimodal (visual and tactile) areas connected wit...
Source: Computational Intelligence and Neuroscience - October 22, 2009 Category: Neuroscience Source Type: journals

NeuroMath: Advanced Methods for the Estimation of Human Brain Activity and ConnectivityEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - October 19, 2009 Category: Neuroscience Source Type: journals

On the Use of Electrooculogram for Efficient Human Computer InterfacesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as &#...
Source: Computational Intelligence and Neuroscience - October 15, 2009 Category: Neuroscience Source Type: journals

Efficient Identification of Assembly Neurons within Massively Parallel Spike TrainsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neuro...
Source: Computational Intelligence and Neuroscience - September 29, 2009 Category: Neuroscience Source Type: journals

Consistent Recovery of Sensory Stimuli Encoded with MIMO Neural CircuitsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We consider the problem of reconstructing finite energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons. The reconstructed signal satisfies a consistency condition: when passed through the same neuron, it triggers the same spike train as the original stimulus. The recovered stimulus has to also minimize a quadratic smoothness optimality criterion. We formulate the reconstruction as a spline interpolation problem for scalar as well as vector valued stimuli and show that the recovery has a unique solution. We provide explicit reconstruction algorithms for stimuli encoded with single as well as a...
Source: Computational Intelligence and Neuroscience - September 22, 2009 Category: Neuroscience Source Type: journals

A Theoretical Investigation of the Relationship between Structural Equation Modeling and Partial Correlation in Functional MRI Effective ConnectivityEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
An important field of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is the investigation of effective connectivity, that is, the actions that a given set of regions exert on one another. We recently proposed a data-driven method based on the partial correlation matrix that could provide some insight regarding the pattern of functional interaction between brain regions as represented by structural equation modeling (SEM). So far, the efficiency of this approach was mostly based on empirical evidence. In this paper, we provide theoretical fundaments explaining why and in what measure struct...
Source: Computational Intelligence and Neuroscience - August 25, 2009 Category: Neuroscience Source Type: journals

The Role of Computational Fluid Dynamics in the Management of Unruptured Intracranial Aneurysms: A Clinicians' ViewEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Conclusions. Although participants showed a manifest interest in CFD, there was a clear lack of awareness concerning the role of hemodynamics in the etiopathogenesis of IAs and the use of CFD in this context. More efforts therefore are required to enhance understanding of the clinicians in the subject. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - August 19, 2009 Category: Neuroscience Source Type: journals

EEG/MEG Source Imaging: Methods, Challenges, and Open IssuesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We present the four key areas of research—preprocessing, the volume conductor, the forward problem, and the inverse problem—that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - July 20, 2009 Category: Neuroscience Source Type: journals

A Framework Combining Delta Event-Related Oscillations (EROs) and Synchronisation Effects (ERD/ERS) to Study Emotional ProcessingEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Event-Related Potentials (ERPs) or Event-Related Oscillations (EROs) have been widely used to study emotional processing, mainly on the theta and gamma frequency bands. However, the role of the slow (delta) waves has been largely ignored. The aim of this study is to provide a framework that combines EROs with Event-Related Desynchronization (ERD)/Event-Related Synchronization (ERS), and peak amplitude analysis of delta activity, evoked by the passive viewing of emotionally evocative pictures. Results showed that th...
Source: Computational Intelligence and Neuroscience - July 9, 2009 Category: Neuroscience Source Type: journals

Seizure (Ictal)—EEG Characteristics in Subgroups of Depressive Disorder in Patients Receiving Electroconvulsive Therapy (ECT)—A Preliminary Study and Multivariate ApproachEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Conclusion. Psychotic depressed patients differ from bipolar depression in their frequency based on probability distribution of ictal EEG. Psychotic depressed patients show more prominent slowing of EEG than nonpsychotic depressed patients. Thus the EEG results may be supportive in classifying subgroups of depression already at the start of the ECT treatment. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

Multimodal Imaging of Human Brain Activity: Rational, Biophysical Aspects and Modes of IntegrationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review s...
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

The Neuroelectromagnetic Inverse Problem and the Zero Dipole Localization ErrorEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
This study aims to help researchers to better guide their choices by clarifying what is hidden behind inverse solutions oversold by their apparently optimal properties to localize single sources. Here, we introduce an inverse solution (ANA) attaining perfect localization of single sources to illustrate how spurious sources emerge and destroy the reconstruction of simultaneously active sources. Although ANA is probably the simplest and robust alternative for data generated by a single dominant source plus noise, the main contribution of this manuscript is to show that zero localization error of single sources is a trivial a...
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG TechnologyEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We estimate cortical activity in normal subjects during the observation of TV commercials inserted within a movie by using high-resolution EEG techniques. The brain activity was evaluated in both time and frequency domains by solving the associate inverse problem of EEG with the use of realistic head models. In particular, we recover statistically significant information about cortical areas engaged by particular scenes inserted within the TV commercial proposed with respect to the brain activity estimated while watching a documentary. Results obtained in the population investigated suggest that the statistically significa...
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

Signatures of Depression in Non-Stationary Biometric Time SeriesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed open questions with regard to analyses and models of mental disorders, in particular depression. The content of this paper emerged from these discussions and in the presentation we briefly link biomarkers (hormones), bio-signals (EEG) and biomaps (brain-maps via EEG) to depression and its treatments, via linear statistical models ...
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

Changes in EEG Power Spectral Density and Cortical Connectivity in Healthy and Tetraplegic Patients during a Motor Imagery TaskEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each R...
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

On the Relation between Bursts and Dynamic Synapse Properties: A Modulation-Based AnsatzEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
When entering a synapse, presynaptic pulse trains are filtered according to the recent pulse history at the synapse and also with respect to their own pulse time course. Various behavioral models have tried to reproduce these complex filtering properties. In particular, the quantal model of neurotransmitter release has been shown to be highly selective for particular presynaptic pulse patterns. However, since the original, pulse-iterative quantal model does not lend itself to mathematical analysis, investigations have only been carried out via simulations. In contrast, we derive a comprehensive explicit expression for the ...
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

Measurement of Brain Function of Car Driver Using Functional Near-Infrared Spectroscopy (fNIRS)Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The aim of this study is to propose a method for analyzing measured signal obtained from functional Near-Infrared Spectroscopy (fNIRS), which is applicable for neuroimaging studies for car drivers. We developed a signal processing method by multiresolution analysis (MRA) based on discrete wavelet transform. Statistical group analysis using Z-score is conducted after the extraction of task-related signal using MRA. Brain activities of subjects with different level of mental calculation are measured by fNIRS and fMRI. Results of mental calculation with nine subjects by using fNIRS and fMRI showed that the proposed methods we...
Source: Computational Intelligence and Neuroscience - June 25, 2009 Category: Neuroscience Source Type: journals

Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation StudyEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In conclusion, our simulation study of canonical decomposition of ictal scalp EEG allowed a robust and accurate localisation of the ictal onset zone. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural NetworksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is ...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Removing Ocular Movement Artefacts by a Joint Smoothened Subspace EstimatorEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG) by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE) calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power). It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect t...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

A Framework to Support Automated Classification and Labeling of Brain Electromagnetic PatternsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We describe recent progress on four goals: 1) specification of rules and concepts that capture expert knowledge of event-related potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification ...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

High-Resolution Movement EEG ClassificationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System FeaturesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Res...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

The P300 as a Marker of Waning Attention and Error PropensityEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Action errors can occur when routine responses are triggered inappropriately by familiar cues. Here, EEG was recorded as volunteers performed a “go/no-go” task of long duration that occasionally and unexpectedly required them to withhold a frequent, routine response. EEG components locked to the onset of relevant go trials were sorted according to whether participants erroneously responded to immediately subsequent no-go trials or correctly withheld their responses. Errors were associated with a significant relative reduction in the amplitude of the preceding P300, that is, a judgement could be made bout whethe...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Brain-Computer Interfaces: Towards Practical Implementations and Potential ApplicationsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

EEG/MEG Signal ProcessingEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Gene Tree Labeling Using Nonnegative Matrix Factorization on Biomedical LiteratureEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Identifying functional groups of genes is a challenging problem for biological applications. Text mining approaches can be used to build hierarchical clusters or trees from the information in the biological literature. In particular, the nonnegative matrix factorization (NMF) is examined as one approach to label hierarchical trees. A generic labeling algorithm as well as an evaluation technique is proposed, and the effects of different NMF parameters with regard to convergence and labeling accuracy are discussed. The primary goals of this study are to provide a qualitative assessment of the NMF and its various parameters a...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Nonnegative Matrix Factorization with Gaussian Process PriorsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors. We assume that the nonnegative factors in the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance function. This allows us to find NMF decompositions that agree with our prior knowledge of the distribution of the factors, such as sparseness, smoothness, and symmetries. The method is demonstrated with an example from chemical shift brain imaging. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Asymmetric Variate Generation via a Parameterless Dual Neural Learning AlgorithmEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In a previous work (S. Fiori, 2006), we proposed a random number generator based on a tunable non-linear neural system, whose learning rule is designed on the basis of a cardinal equation from statistics and whose implementation is based on look-up tables (LUTs). The aim of the present manuscript is to improve the above-mentioned random number generation method by changing the learning principle, while retaining the efficient LUT-based implementation. The new method proposed here proves easier to implement and relaxes some previous limitations. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Probabilistic Latent Variable Models as Nonnegative FactorizationsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statistical models for analyzing nonnegative data. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Theorems on Positive Data: On the Uniqueness of NMFEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not. The theorems are illustrated by several examples showing the use of the theorems and their limitations. We have shown that corruption of a unique NMF matrix by additive noise leads to a noisy estimation of the noise-free unique solution. Finally, we use a stochastic view of NMF to analyze which characterization of the underlying model will result in an NMF with small estimation errors. (Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Performance of a Self-Paced Brain Computer Interface on Data Contaminated with Eye-Movement Artifacts and on Data Recorded in a Subsequent SessionEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The performance of a specific self-paced BCI (SBCI) is investigated using two different datasets to determine its suitability for using online: (1) data contaminated with large-amplitude eye movements, and (2) data recorded in a session subsequent to the original sessions used to design the system. No part of the data was rejected in the subsequent session. Therefore, this dataset can be regarded as a “pseudo-online” test set. The SBCI under investigation uses features extracted from three specific neurological phenomena. Each of these neurological phenomena belongs to a different frequency band. Since many pro...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Robust Object Recognition under Partial Occlusions Using NMFEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In recent years, nonnegative matrix factorization (NMF) methods of a reduced image data representation attracted the attention of computer vision community. These methods are considered as a convenient part-based representation of image data for recognition tasks with occluded objects. A novel modification in NMF recognition tasks is proposed which utilizes the matrix sparseness control introduced by Hoyer. We have analyzed the influence of sparseness on recognition rates (RRs) for various dimensions of subspaces generated for two image databases, ORL face database, and USPS handwritten digit database. We have studied the ...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Single-Trial Decoding of Bistable Perception Based on Sparse Nonnegative Tensor DecompositionEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative tensor factorization-(NTF)-based method to extract features from the local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequenc...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Extended Nonnegative Tensor Factorisation Models for Musical Sound Source SeparationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmonicity constraints on the recovered basis functions. This paper proposes a new additive synthesis-based approach which allows the use of linear-frequency spectrograms as well as imposing strict harmonic constraints, resulting in an i...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Pattern Expression Nonnegative Matrix Factorization: Algorithm and Applications to Blind Source SeparationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Independent component analysis (ICA) is a widely applicable and effective approach in blind source separation (BSS), with limitations that sources are statistically independent. However, more common situation is blind source separation for nonnegative linear model (NNLM) where the observations are nonnegative linear combinations of nonnegative sources, and the sources may be statistically dependent. We propose a pattern expression nonnegative matrix factorization (PE-NMF) approach from the view point of using basis vectors most effectively to express patterns. Two regularization or penalty terms are introduced to be added ...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental TasksEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and th...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Advances in Nonnegative Matrix and Tensor FactorizationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: Computational Intelligence and Neuroscience)
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale ProblemsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Recently, a considerable growth of interest in projected gradient (PG) methods has been observed due to their high efficiency in solving large-scale convex minimization problems subject to linear constraints. Since the minimization problems underlying nonnegative matrix factorization (NMF) of large matrices well matches this class of minimization problems, we investigate and test some recent PG methods in the context of their applicability to NMF. In particular, the paper focuses on the following modified methods: projected Landweber, Barzilai-Borwein gradient projection, projected sequential subspace optimization (PSESOP)...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled EpilepsyEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subject...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain CommunicationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a scr...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord InjuryEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean ± SD) were obtained: left versus right hand 65.03% ± 8.52, left hand versus feet 68.19% ± 11.08, and right hand versus feet 65.05% ± 9.25...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Unsupervised Learning of Overlapping Image Components Using Divisive Input ModulationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Some Computational Aspects of the Brain Computer Interfaces Based on Inner MusicEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We discuss the BCI based on inner tones and inner music. We had some success in the detection of inner tones, the imagined tones which are not sung aloud. Rather easily imagined and controlled, they offer a set of states usable for BCI, with high information capacity and high transfer rates. Imagination of sounds or musical tunes could provide a multicommand language for BCI, as if using the natural language. Moreover, this approach could be used to test musical abilities. Such BCI interface could be superior when there is a need for a broader command language. Some computational estimates and unresolved difficulties are p...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Bayesian Inference for Nonnegative Matrix Factorisation ModelsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We describe nonnegative matrix factorisation (NMF) with a Kullback-Leibler (KL) error measure in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior leads to the standard KL-NMF algorithms as special cases, where maximum likelihood parameter estimation is carried out via the Expectation-Maximisation (EM) algorithm. Starting from this view, we develop full Bayesian inference via variational Bayes or Monte Carlo. Our construction retains conjugacy and enables us to develop more powerful models while retaining attractive features of standard NMF ...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Information Infrastructure for Cooperative Research in NeuroscienceEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The paper describes a framework for efficient sharing of knowledge between research groups, which have been working for several years without flaws. The obstacles in cooperation are connected primarily with the lack of platforms for effective exchange of experimental data, models, and algorithms. The solution to these problems is proposed by construction of the platform (EEG.pl) with the semantic aware search scheme between portals. The above approach implanted in the international cooperative projects like NEUROMATH may bring the significant progress in designing efficient methods for neuroscience research. (Source: Compu...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals

Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%)...
Source: Computational Intelligence and Neuroscience - June 10, 2009 Category: Neuroscience Source Type: journals