Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a
rather blurred image of brain activity. Therefore spatial filters are extremely useful in single …

Machine learning and pattern recognition algorithms have in the past years developed to
become a working horse in brain imaging and the computational neurosciences, as they are …

Recent evidence from functional magnetic resonance imaging suggests that cortical
hemodynamic responses coincide in different subjects experiencing a common naturalistic …

In this paper, we describe a simple set of “recipes” for the analysis of high spatial density
EEG. We focus on a linear integration of multiple channels for extracting individual …

Machine learning offers a principled approach for develo** sophisticated, automatic, and
objective algorithms for analysis of high-dimensional and multimodal biomedical data. This …

We performed simultaneous recordings of electroencephalography (EEG) from multiple
students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked …

We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial
filtering in the context of brain-computer interfaces (BCIs) based on electroencephalography …

Attentional engagement is a major determinant of how effectively we gather information
through our senses. Alongside the sheer growth in the amount and variety of information …

For decades, the frontal alpha asymmetry (FAA)–a disproportion in EEG alpha oscillations
power between right and left frontal channels–has been one of the most popular measures …

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel
electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a …