1,721,065 research outputs found
An EEG study on civil pilots during flight simulation
No full textOBIETTIVI
Misurazioni neurofisiologiche e relative features possono essere usate ai fini di caratterizzare differenti stati mentali e di stimare l’attività del sistema nervoso centrale legata all’esecuzione di task di diversa natura. Lo scopo del presente studio è quello di usare i segnali elettroencefalografici (EEG) e metodi di stima della connettività cerebrale per investigare lo sforzo mentale richiesto durante diverse fasi di una simulazione di volo su piloti civili.
METODI
Le registrazioni EEG e la stima della connettività sono state eseguite su piloti dell’aviazione civile (Comandanti e Primi Ufficiali) durante un volo in un simulatore professionale. La connettività cerebrale è stata stimata per mezzo della Partial Directed Coherence e misure sintetiche sono state estratte dai pattern ottenuti al fine di descrivere l’attività cerebrale elicitata durante le diverse fasi di volo.
RISULTATI
Durante le fasi di decollo ed atterraggio, un denso pattern di comunicazione tra le aree cerebrali è stato rilevato sia per i Capitani che per i Primi Ufficiali. Durante la fase di crociera invece, queste reti cerebrali diventano più sparse e caratterizzate da una comunicazione meno efficiente.
CONCLUSIONI
I risultati ottenuti hanno mostrato che le registrazioni EEG e lo studio della connettività cerebrale sono potenti strumenti di indagine per la comprensione dei meccanismi sottostanti l’esecuzione di compiti come un volo simulato.OBJECTIVES
Neurophysiological measurements and their related features can be used to characterize different mental states and to estimate the activity of the central nervous system related to the execution of tasks of different nature. The aim of the present study is to use electroencephalographic (EEG) signals and brain connectivity estimation to investigate the mental effort required during different phases of a flight simulation on civil pilots.
METHODS
EEG recordings and connectivity estimation were performed on civil aviation pilots (Captains and First Officers) during a flight in a professional simulator. Brain connectivity was estimated by means of Partial Directed Coherence and synthetic measures were extracted from the achieved patterns to describe the brain activity elicited during different flight phases.
RESULTS
In the take-off and landing phases, a dense communication between cerebral areas was detected both for Captains and First Officers. In contrast, these patterns of connections become sparser during the cruise phase. Moreover, the network organization during cruise phase results less efficient than in the other flight phases.
CONCLUSIONS
The obtained results have shown that the EEG recordings and brain connectivity study are powerful tools of investigation for the understanding of the neural mechanisms underlying the execution of tasks like a flight simulation
Measuring Connectivity in Linear Multivariate Processes With Penalized Regression Techniques
Full text linkThe evaluation of time and frequency domain measures of coupling and causality relies on
the parametric representation of linear multivariate processes. The study of temporal dependencies among
time series is based on the identification of a Vector Autoregressive model. This procedure is pursued
through the definition of a regression problem solved by means of Ordinary Least Squares (OLS) estimator.
However, its accuracy is strongly influenced by the lack of data points and a stable solution is not always
guaranteed. To overcome this issue, it is possible to use penalized regression techniques. The aim of this
work is to compare the behavior of OLS with different penalized regression methods used for a connectivity
analysis in different experimental conditions. Bias, accuracy in the reconstruction of network structure
and computational time were used for this purpose. Different penalized regressions were tested by means
of simulated data implementing different ground-truth networks under different amounts of data samples
available. Then, the approaches were applied to real electroencephalographic signals (EEG) recorded from
a healthy volunteer performing a motor imagery task. Penalized regressions outperform OLS in simulation
settings when few data samples are available. The application on real EEG data showed how it is possible
to use features extracted from brain networks for discriminating between two tasks even in conditions of
data paucity. Penalized regression techniques can be used for brain connectivity estimation and can be
exploited for the computation of all the connectivity estimators based on linearity assumption overcoming
the limitations imposed by the classical OLS
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
Full text linkOne of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a statespace (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Square (OLS) estimation, a viable alternative is to use Artificial Neural Networks (ANNs) implemented in a simple structure with one input and one output layer and trained in a way such that the weights matrix corresponds to the matrix of VAR parameters. In this work, we introduce an ANN combined with SS models for the computation of GC. The ANN is trained through the Stochastic Gradient Descent L1 (SGD-L1) algorithm, and a cumulative penalty inspired from penalized regression is applied to the network weights to encourage sparsity. Simulating networks of coupled Gaussian systems, we show how the combination of ANNs and SGD-L1 allows to mitigate the strong reduction in accuracy of OLS identification in settings of low ratio between number of time series points and of VAR parameters. We also report how the performances in GC estimation are influenced by the number of iterations of gradient descent and by the learning rate used for training the ANN. We recommend using some specific combinations for these parameters to optimize the performance of GC estimation. Then, the performances of ANN and OLS are compared in terms of GC magnitude and statistical significance to highlight the potential of the new approach to reconstruct causal coupling strength and network topology even in challenging conditions of data paucity. The results highlight the importance of of a proper selection of regularization parameter which determines the degree of sparsity in the estimated network. Furthermore, we apply the two approaches to real data scenarios, to study the physiological network of brain and peripheral interactions in humans under different conditions of rest and mental stress, and the effects of the newly emerged concept of remote synchronization on the information exchanged in a ring of electronic oscillators. The results highlight how ANNs provide a mesoscopic description of the information exchanged in networks of multiple interacting physiological systems, preserving the most active causal interactions between cardiovascular, respiratory and brain systems. Moreover, ANNs can reconstruct the flow of directed information in a ring of oscillators whose statistical properties can be related to those of physiological network
Time-varying effective connectivity for investigating the neurophysiological basis of cognitive processes
No full textThis chapter describes the methodological advancements developed during the last 20 years in the field of effective connectivity based on Granger causality and linear autoregressive modeling. At first we introduce the concept of Granger causality and its application to the connectivity field. Then, a detailed description of both stationary and time-varying versions of Partial Directed Coherence (PDC) estimator for effective connectivity will be given. The General Linear Kalman Filter (GLKF) approach is described an algorithm, recently introduced for estimating the temporal evolution of the parameters of adaptive multivariate model, able to overcome the limits of existing time-varying approaches. Then a detailed description of the graph theory approach and of possible indexes which could be defined is given. At the end, the potentiality of the described methodologies is demonstrated in an application aiming at investigating the neurophysiological basis of motor imagery processes
Testing the significance of connectivity networks: Comparison of different assessing procedures
No full textDespite the well-established use of partial directed coherence (PDC) to estimate interactions between brain signals, the assessment of its statistical significance still remains controversial. Commonly used approaches are based on the generation of empirical distributions of the null case, implying a considerable computational time, which may become a serious limitation in practical applications. Recently, rigorous asymptotic distributions for PDC were proposed. The aim of this work is to compare the performances of the asymptotic statistics with those of an empirical approach, in terms of both accuracy and computational time. Methods: Indices of performance were derived for the two approaches by a simulation study implementing different ground-truth networks under different levels of signal-to-noise ratio and amount of data available for the estimate. The two approaches were then applied to the resting-state EEG data acquired in a group of minimally conscious state and vegetative state/unresponsive wakefulness syndrome patients. Results: The performances of the asymptotic statistics in simulations matched those obtained by the empirical approach, with a considerable reduction of the computational time. Results of the application to real data showed that the asymptotic statistics led to the extraction of connectivity-based indices able to discriminate patients in different disorders of consciousness conditions and to correlate significantly with clinical scales. Such results were similar to those obtained by the empirical assessment, but with a considerable time economy. Significance: Asymptotic statistics provide an approach to the assessment of PDC significance with comparable performances with respect to the previously used empirical approaches but with a substantial advantage in terms of computational time
P300-based Brain-Computer Interface for communication in assistive technology centers: influence of users’ profile on BCI access
No full textObjective. Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A Brain-Computer Interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-center to investigate their eligibility for BCI access and the factors influencing the BCI control.
Approach. Thirty-five users and 11 healthy controls were included in the study. Participants were required to operate a P300-speller BCI. We investigated differences in BCI performance metrics (online accuracy and Information Transfer Rate) between end-user and control groups and we evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance.
Main results. 7.1% of the users controlled the system with a mean accuracy of 93.6±8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR.
Significance. The percentage of users who had an accuracy considered as functional communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and to the factors influencing (and not influencing) it, are a contribution to the process of introducing BCIs in the AT-centers, considering the BCI for communication as an additional input to provide multimodal access to AT
Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface
Full text linkHybrid Brain-Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of "more normal" brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation
EEG-based indices as outcome measures for a memory rehabilitation treatment in stroke patients
No full textThe efficacy of cognitive rehabilitation treatments after stroke is routinely assessed by means of neuropsychological tests battery. More evidences indicate that the neuroplasticity phenomena which occurs after stroke can be characterized by investigating brain networks changes. Despite the efforts in the field, a complete description of connectivity patterns characterizing different phases of cognitive recovery in stroke patients is still missing. In this work, we proposed a combined approach of advanced methodologies for effective connectivity estimation and graph theory for defining EEG-based descriptors able to: i) characterize the brain processes at the basis of a memory rehabilitation treatment and ii)support its clinical evaluation. We derived neurophysiological indices from a previous study on healthy subjects and then we used them as outcome measures of a rehabilitation treatment on stroke-patients.The efficacy of cognitive rehabilitation treatments after stroke is routinely assessed by means of neuropsychological tests battery. More evidences indicate that the neuroplasticity phenomena which occurs after stroke can be characterized by investigating brain networks changes. Despite the efforts in the field, a complete description of connectivity patterns characterizing different phases of cognitive recovery in stroke patients is still missing. In this work, we proposed a combined approach of advanced methodologies for effective connectivity estimation and graph theory for defining EEG-based descriptors able to: i) characterize the brain processes at the basis of a memory rehabilitation treatment and ii)support its clinical evaluation. We derived neurophysiological indices from a previous study on healthy subjects and then we used them as outcome measures of a rehabilitation treatment on stroke-patients
- …
