Neural basis for the brain responses to the marketing messages: an high resolution EEG study.

We investigated the behaviour of the brain during the visualization of commercial videos by tracking the cortical activity and the functional connectivity changes in normal subjects. High resolution EEG recordings were performed in a group of healthy subjects, and the cortical activity during the visualization of standard commercial spots and emotional spots (no profit companies) was estimated by using the solution of the linear inverse problem with the use of realistic head models. The cortical activity was evaluated in several regions of interest (ROIs) coincident with the Brodmann areas.

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies.

The Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC) are frequency-domain estimators, based on the multivariate autoregressive modelling (MVAR) of time series, that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods requires the stationary of the signals. In this way, transient pathways of information transfer remains hidden.

Removal of ocular artifacts for high resolution EEG studies: a simulation study.

Eye movements and blinks may produce unusual voltage changes that propagates from the eyeball through the head as volume conductor up to the scalp electrodes, generating severe electroencephalographic artifacts. Several methods are now available to correct the distortion induced by these events on the EEG, having different advantages and drawbacks. The main focus of this work is to quantify the performance of the removal of EOG artifact due to the application of the independent component analysis (ICA) methodology.

Autoregressive spectral analysis in Brain Computer Interface context.

Over the past decade, a number of studies have evaluated the possibility that scalp-recorded electroencephalogram (EEG) activity might be the basis for a brain-computer interface (BCI), a system able to determine the intent of the user from a variety of different electrophysiological signals. With our current EEG-based communication system, users learn over a series of training sessions to use EEG to move a cursor on a video screen: to make this possible users must learn to control the EEG features that determines cursor movement and we must improve signal processing methods to extract from background noise the EEG features that the system translates into cursor movement. Non-invasive data acquisition, makes automated feature extraction challenging, since the signals of interest are "hidden" in a highly noisy environment.

Hypermethods for EEG hyperscanning.

Until now, in EEG studies the activity of the brain during simple or complex tasks have been recorded in a single subject. Often, during such EEG recordings, subjects interacts with the external devices or the researchers in order to reproduce conditions similar to the those usually occurring in the real-life. However, in order to study the concurrent activity in subjects interacting in cooperation or competition activities, the issue of the simultaneous recording of their brain activity became mandatory.