F. Babiloni

High resolution EEG: A new model-dependent spatial deblurring method using a realistically-shaped MR-constructed subject's head model

This paper presents a new model-dependent method for the spatial deblurring of scalp-recorded EEG potentials based on boundary-element and cortical imaging techniques. This model-dependent spatial deblurring (MDSD) method used MR images for the reconstruction of the subjects head model, and a layer of 364 radially-oriented equivalent current dipoles as a source model. The validation of the MDSD method was performed on simulated potential distributions generated from equivalent dipoles oriented radially, obliquely, and tangentially to the head surface. Furthermore, this method was used to localize neocortical sources of human movement-related and somatosensory-evoked potentials. It was shown that the new MDSD method improved markedly the spatial resolution of the simulated surface potentials and scalp-recorded event-related potentials. The spatial information content of the scalp-recorded EEG potentials increased progressively by increasing the spatial sampling from 28 to 128 channels. These results indicate that the new method could be satisfactorily used for high resolution EEG studies.

Performances of surface Laplacian estimators: A study of simulated and real scalp potential distributions

SummaryThis paper presents a study of the performance of various local and spherical spline methods currently in use for the surface Laplacian (SL) estimate of scalp potential distributions. The SL was estimated from simulated instantaneous event-related scalp potentials generated over a three-shell spherical head model. Laplacian estimators used planar and spherical scalp models. Noise of increasing magnitude and spatial frequency was added to the potential distributions in order to simulate noise presumed to contaminate scalp-recorded event-related potentials. A comparison of noise effects on various Laplacian estimates was made for increasing number of “electrode” positions in variants of the 10–20 system. Furthermore, to evaluate the error due to the use of unrealistic scalp models, the matching between SL estimates of human scalp-recorded movement-related potentials computed on spherical and realistically-shaped MRI-constructed models of the scalp was examined. With all methods the error of the SL estimate increased proportionally with the magnitude and spatial frequency of noise. Increased number of “electrodes” up to 256 significantly reduced the error (p<0.05). In general, the best SL estimates were computed by second and third order splines including λ correction, the performances of the second order spline being better with more than 64 “electrodes”. Compared with spline Lapladans, the best local methods provided nearly equal estimates with low spatial sampling (19 and 28 “electrodes”), as well as high spatial frequency noise. The error of the SL estimate due to unrealistic scalp model was significant, and it augmented with increased spatial sampling from 64 to 128 electrodes.

High-resolution electro-encephalogram: source estimates of Laplacian-transformed somatosensory-evoked potentials using a realistic subject head model constructed from magnetic resonance images

A novel high-resolution electro-encephalographic (EEG) procedure is proposed, including high spatial sampling (128 channels), a realistic magnetic resonance-constructed subject head model, a multi-dipole cortical source model and regularised weighted minimum-norm linear inverse source estimation (WMN). As an innovation, EEG potentials (two healthy subjects; median-nerve, short-latency somatosensory-evoked potentials (SEPs)) are preliminarily Laplacian-transformed (LT) to remove brain electrical activity generated by subcortical sources (i.e. not represented in the source model). LT-WMN estimates are mathematically evaluated by figures of merit (WMN estimates as a reference). Results show higher dipole identifiability (0.69;0.88), lower dipole localisation error (0.6 mm; 7.8mm) and lower spatial dispersion (8.6 mm; 24mm) in LT-WMN than in WMN estimates (Bonferroni corrected p<0.001). These estimates are presented on the subject modelled cortical surface to highlight the increased spatial information content in LT-WMN compared with WMN estimates. The proposed high-resolution EEG technique is useful for the study of somatosensory functions in basic research and clinical applications.

IMPROVED REALISTIC LAPLACIAN ESTIMATE OF HIGHLY-SAMPLED EEG POTENTIALS BY REGULARIZATION TECHNIQUES

In this study we investigated the effects of lambda correction, generalized cross-validation (GCV), and Tikhonov regularization techniques on the realistic Laplacian (RL) estimate of highly-sampled (128 channels) simulated and actual EEG potential distributions. The simulated EEG potential distributions were mathematically generated over a 3-shell spherical head model (analytic potential distributions). Noise was added to the analytic potential distributions to mimic EEG noise. The magnitude of the noise was 20, 40 and 80% that of the analytic potential distributions. Performance of the regularization techniques was evaluated by computing the root mean square error (RMSE) between regularized RL estimates and analytic surface Laplacian solutions. The actual EEG data were human movement-related and short-latency somatosensory-evoked potentials. The RL of these potentials was estimated over a realistically-shaped, magnetic resonance-constructed model of the subjects scalp surface. The RL estimate of the simulated potential distributions was improved with all the regularization techniques. However, the lambda correction and Tikhonov regularization techniques provided more precise Laplacian solutions than the GCV computation (P < 0.05); they also improved better than the GCV computation the spatial detail of the movement-related and short-latency somatosensory-evoked potential distributions. For both simulated and actual EEG potential distributions the Tikhonov and lambda correction techniques provided nearly equal Laplacian solutions, but the former offered the advantage that no preliminary simulation was required to regularize the RL estimate of the actual EEG data.

Human Brain Distinctiveness Based on EEG Spectral Coherence Connectivity

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.

Cortical Activity and Connectivity of Human Brain during the Prisoner's Dilemma: an EEG Hyperscanning Study

A major limitation of the approaches used in most of the studies performed so far for the characterization of the brain responses during social interaction is that only one of the participating brains is measured each time. The ldquointeractionrdquo between cooperating, competing or communicating brains is thus not measured directly, but inferred by independent observations aggregated by cognitive models and assumptions that link behavior and neural activation. In this paper, we use the simultaneous neuroelectric recording of several subjects engaged in cooperative games (EEG hyperscanning). This EEG hyperscanning allow us to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. We used a paradigm called Prisoners Dilemma derived from the game theory. Results collected in a population of 22 subjects suggested that the most consistently activated structure in social interaction paradigms is the medial prefrontal cortex, which is found to be active in all the conflict situations analyzed. The role of the anterior cingulated cortex (ACC) assumes a main character being a discriminant factor for the ldquodefectrdquo attitude of the entire population examined. This observation is compatible with the role that the Theory of Mind assigns to the ACC.

Mahalanobis distance-based classifiers are able to recognize EEG patterns by using few EEG electrodes

We explore the use of quadratic classifiers based on Mahalanobis distance to detect EEG patterns from a reduced set of recording electrodes. Such classifiers used the diagonal and full covariance matrix of EEG spectral features extracted from EEG data. Such data were recorded from a group of 8 healthy subjects with 4 electrodes, placed in C3, P3, C4, P4 position of the international 10-20 system. Mahalanobis distance classifiers based on the use of full covariance matrix are able to detect EEG activity related to the imagination of movement with affordable accuracy (average score 98%). Reported average recognition data were obtained by using the cross-validation of the EEG recordings for each subject. Such results open the avenue for the use of Mahalanobis-based classifiers in a brain computer interface context, in which the use of a reduced set of recording electrodes is an important issue.

Adaptive brain interfaces for physically-disabled people

This paper presents first results of an adaptive brain interface suitable for deployment outside controlled laboratory settings. It robustly recognizes three purely mental states from on-line spontaneous EEG signals and has them associated to simple commands. Three commands allow to interact intelligently with a computer-based system through task decomposition. Our approach seeks to develop individual interfaces since no two people are the same either physiologically or psychologically. Thus the interface adapts to its owner as its neural classifier learns user-specific filters.

A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases

An efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amiotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. Often, such diseases leave the ocular movements preserved for a relatively long time. The aim of this study is to present a new approach for the hybrid system which is based on the recognition of electrooculogram (EOG) and electroencephalogram (EEG) measurements for efficient communication and control. As a first step we show that the EOG-based side of the system for communication and controls is useful for patients. The EOG side of the system has been equipped with an interface including a speller to notify of messages. A comparison of the performance of the EOG-based system has been made with a BCI system that uses P300 waveforms. As a next step, we plan to integrate EOG and EEG sides. The final goal of the project is to realize a unique noninvasive device able to offer the patient the partial restoration of communication and control abilities with EOG and EEG signals.

Brain waves based user recognition using the “eyes closed resting conditions” protocol

In this paper the use of brain waves as a biometric identifier is investigated. Among the very different protocols that can be used to acquire the electroencephalogram signal (EEG) of an individual we rely on a very simple one: closed eyes in resting conditions. A database of 48 healthy subjects, collected by the authors at the neurophysiology laboratory of the IRCCS Fondazione Santa Lucia, Roma, Italy, has been used for the experiments. Signals acquired from triplets of electrodes have been employed in the experimentations. In more detail, ten different triplets have been used separately in the experiments in order to speculate about the most suitable triplet to capture the occurring phenomena. Feature vectors constituted by the reflection coefficients of a six order AR model have been extracted for each used channel thus giving rise to a feature vector of length eighteen. A polynomial regression based classification is then employed. This analysis has been performed for three different frequency bands for each of the ten different triplet under analysis. The obtained genuine acceptance rate is of 96.08%.