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. It was demonstrated that the spatial filtering operations improve the signal-to-noise ratio. On the contrary, autoregressive modeling has been successfully used by many investigators for EEG signals analysis in BCI context, but to our knowledge no clear guidelines exist on how to choose the parameters of the spectral estimation. Here we present an analysis of the dependence of BCI performance on the parameters of the feature extraction algorithm. In order to optimize user performances, we observed that a different model order value had to be chosen correspondently to different EEG features used to control the system, according to the differences in the spectral power content of alpha and/or beta bands.

Neuroelectrical source imaging of mu rhythm control for BCI applications.

In the last decade, the possibility to noninvasively estimate cortical activity has been highlighted by the application of the techniques known as high resolution EEG. These techniques include a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multi-dipole source model, and regularized linear inverse source estimates of cortical current density. The aim of this paper is to demonstrate that the use of cortical activity estimated from noninvasive EEG recordings of motor imagery is useful in the context of a brain computer interface as compared with others scalp spatial filters usually used on-line.

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. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR). This approach will allow the observation of transient influences between the cortical areas during the execution of a task. Time-varying DTF and PDC were obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Simulations were performed under different levels of Signal to Noise Ratio (SNR), number of trials (TRIALS) and frequency bands (BAND), and of different values of the RLS adaptation factor adopted (factor C). The results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of SNR ad number of trials. Moreover, the capability of follow the rapid changes in connectivity is highly increased by the number of trials at disposal, and by the right choice of the value adopted for the adaptation factor C. The results of the simulation study indicate that DTF and PDC computed on adaptive MVAR can be effectively used to estimate time-varying patterns of functional connectivity between cortical activations, under general conditions met in practical EEG recordings.

Brain computer interface: estimation of cortical activity from non invasive high resolution EEG recordings.

The aim of this paper is to analyze whether the use of the cortical activity estimated from non invasive EEG recordings could be useful to detect mental states related to the imagination of limb movements. Estimation of cortical activity was performed on high resolution EEG data related to the imagination of limb movements gathered in five normal healthy subjects by using realistic head models. Cortical activity was estimated in region of interest associated with the subject's Brodmann areas by using depth-weighted minimum norm solutions. Comparisons between surface recorded EEG and the estimated cortical activity were performed. The estimated cortical activity related to the mental imagery of limbs in the five subjects is located mainly over the contralateral primary motor area. The unbalance between brain activity estimated in contralateral and ipsilateral motor cortical areas relative to the finger movement imagination is greater than those obtained in the scalp EEG recordings. Results suggest that the use of the estimated cortical activity for the motor imagery of upper limbs could be potentially superior with respect to the use of surface EEG recordings. This is due to a greater statistically significant unbalance between the activity estimated in the contralateral and ipsilateral hemisphere with respect to those observed with surface EEG. These results are useful in the context of the development of a non invasive brain computer interface.