The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Over the last decade, machine learning algorithms have proven to be useful tools for exploring neural representations of percepts and concepts in the brain. An important but often neglected next step is it to relate neural representations to human behavior. Here, we introduce a novel approach to definitively linking neural representations to structural properties of stimuli as well as human behavior...
Decoding human brain activities via functional magnetic resonance imaging (fMRI) has gained increasing attention in recent years. While encouraging results have been reported in brain states classification tasks, reconstructing the details of human visual experience still remains difficult. Two main challenges that hinder the development of effective models are the perplexing fMRI measurement noise...
The canonical correlation analysis (CCA), double-partial least-squares (DPLS) methods and least absolute shrinkage and selection operator (LASSO) have been proven effectively in detecting the steady-state visual evoked potential (SSVEP) in SSVEP-based brain-computer interface systems. However, the accuracy of SSVEP classification can be affected by phase shifts of the electroencephalography data,...
Classification of data is used in data analysis to group various instances in appropriate classes to enhance readability of data and study its characteristics easily. The main aim of every classification problem is the enhancement of classification accuracy. Ranked feature ordering helps in improving the classification accuracy by removing the least dominant features. Classification model uses only...
Why are some people more creative than others? How do human brain networks evolve over time? A key stepping stone to both mysteries and many more is to compare weighted brain networks. In contrast to networks arising from other application domains, the brain network exhibits its own characteristics (e.g., high density, indistinguishability), which makes any off-the-shelf data mining algorithm as well...
Linear predictive models are applied to functional MRI (fMRI) data to estimate boundaries that predict experimental task states for scans. These boundaries are visualized as statistical parametric maps (SPMs) and range from low to high spatial reproducibility across subjects (e.g., Strother <etal/>, 2004; LaConte <etal/>, 2003). Such inter-subject pattern reproducibility is an essential...
With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed...
Autism spectrum disorders (ASDs) are presented as social deficits that cause different behavior abnormalities. One of the most important characteristic of ASDs is repetitive behavior. Recent studies of functional connectivity show that these deficits may alter connections in the brain. Most of them show alternations in default mode network (DMN) only using a model based or model free method. In this...
Discriminating between active and non-active brain voxels in noisy functional magnetic resonance imaging (fMRI) data plays an important role when investigating task-related activations of the neuronal sites. A novel method for efficiently capturing drifts in the functional magnetic resonance imaging (fMRI) data is presented that leads to enhanced fMRI activation detection. The proposed algorithm apply...
Dynamic images of functional activity in the brain offer the potential to measure connectivity between regions of interest. We want to measure causal activity between regions of interest (ROIs) with signals recorded from multiple channels or voxels in each ROI. Previous methods, such as Granger causality, look for causality between individual time series; hence, they suffer from local interactions...
We evaluated neurovascular and autonomic response to a Divided Attention task within a group of 16 healthy subjects, by means of Electroencephalography, Electrocardiography, functional Near Infrared Spectroscopy techniques, acquired simultaneously. We exctracted Alpha (8–13,5 Hz) and Beta (13,5–30 Hz) power rhythms with a spectral autoregressive residual model, and inter-beat-interval (RR series)...
Brain activity patterns as well as anatomical structure differ from person to person. Although anatomical normalization techniques have been used for functional magnetic resonance imaging studies, there are no standard methods to deal with individual differences in activity patterns. In this study, we propose a method to convert brain activity patterns from one person to another by predicting the...
This work proposes a model-free approach to fMRI-based brain mapping where the BOLD response is learnt from data rather than assumed in advance. For each voxel, a paired sequence of stimuli and fMRI recording is given to a supervised learning process. The result is a voxel-wise model of the expected BOLD response related to a set of stimuli. Differently from standard brain mapping techniques, where...
Analysis of single-trial mental EEG may potentially reveal more information about for brain dynamics investigating than simple averaging methods. However, Traditional EEG analysis relies on the averaging methods to increase event related potentials and basic rhythms extraction from EEG, which face many signal processing challenges, such as trial-to-trial variability in latencies, amplitudes of event-related...
Many brain events and disorders can be detected by analyzing electroencephalograms (EEGs). Also the availability of quantitative biological markers that are correlated with qualitative psychiatric phenotypes helps us to utilize automated methods to diagnose and classify these phenotypes. One such a psychiatric phenotype is alcoholism. In this study a method to select an optimal subset of EEG channels...
This work proposes a clustering technique to analyze evoked potential signals. The proposed method uses an orthogonal subspace model to enhance the single-trial signals of a session and simultaneously a subspace measure to group the trials into clusters. The ensemble averages of the signals of the different clusters are compared with ensemble averages of visually selected trials which are free of...
Engineering analysis has been utilized with great success over the past few decades to characterize physiological systems. For example, system identification approaches have been developed to describe the linear and nonlinear properties of such systems in a very general way, allowing for new insights to be made into physiological function. Recent work has seen the application of these techniques to...
We propose a model that describes the interaction of several brain regions based on functional magnetic resonance imaging (FMRI) time series to make inferences about functional integration and segregation within the human brain. The method is demonstrated using dynamic causal modeling (DCM) using real data to show how such models are able to characterize interregional dependence. We extend estimating...
We explore unsupervised, hypothesis-free methods for fMRI analysis in two different types of experiments. First, we employ clustering to identify large-scale functionally homogeneous systems. We formulate a generative mixture model, derive the EM algorithm and apply it to delineate functional systems. We also investigate spectral clustering in application to this problem and demonstrate that both...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.