Common spatial patterns (CSP) is a popular feature extraction method for discriminating between positive and negative classes in electroencephalography (EEG) data. Two probabilistic models for CSP were recently developed: probabilistic CSP (PCSP), which is trained by expectation maximization (EM), and variational Bayesian CSP (VBCSP) which is learned by variational approximation. Parameter expa...

In this study, a novel regularized common spatial pattern method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery based brain computer interfaces. Common Spatial Patterns (CSP) method is an effective spatial filter for discriminating different motor imagery signals acquired using large number of EEG electrodes. Unfortunately, CSP method is...

In this work we evaluate the possibility of predicting the emotional state of a person based on the EEG. We investigate the problem of classifying valence from EEG signals during the presentation of affective pictures, utilizing the ”frontal EEG asymmetry” phenomenon. To distinguish positive and negative emotions, we applied the Common Spatial Patterns algorithm. In contrast to our expectations...

This paper presents a method to recover task-related sources from a multi-class BrainComputer Interface (BCI) based on motor imagery. Our method gathers two common approaches to tackle the multi-class problem: 1) the supervised approach of Common Spatial Pattern (CSP) to discriminate between different tasks; 2) the criterion of statistical independence of non-stationary sources used in Independ...

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor calle...

Common spatial patterns (CSP) is a popular feature extraction method for discriminating between positive and negative classes in electroencephalography (EEG) data. Two probabilistic models for CSP were recently developed: probabilistic CSP (PCSP), which is trained by expectation maximization (EM), and variational Bayesian CSP (VBCSP) which is learned by variational approximation. Parameter expa...

BACKGROUND Brain computer interfaces (BCI) is one of the most popular branches in biomedical engineering. It aims at constructing a communication between the disabled persons and the auxiliary equipments in order to improve the patients' life. In motor imagery (MI) based BCI, one of the popular feature extraction strategies is Common Spatial Patterns (CSP). In practical BCI situation, scalp EEG...

There are often two types of correlations in multivariate spatial data: correlations between variables measured at the same locations, and correlations of each variable across the locations. We hypothesize that these two types of correlations are caused by a common spatially correlated underlying factor. Under this hypothesis, we propose a generalized common spatial factor model. The parameters...

Density is a critical typology in determining sustainable urban built-form patterns. Built-formrefers to the assemblage and arrangement of the building masses in a city reflecting the spatial layout of spaces.The relationship between density and urban character is also based on at certain densities (thresholds). In a widersense, sustainable cities are a matter of density. Recent debates about t...

We use techniques of logic-based relational machine learning to automatically detect spatial patterns common to 21 previously described examples of zinc finger DNA complexes. We demonstrate that such patterns can be found and thus the proposed methodology may potentially serve to achieve better understanding of zinc finger DNA binding. Keywords— Structural Genomics, Machine Learning, Data