Ricerc@Sapienza

Introduction Modern neuroimaging has provided unequivocal evidence that brain functions are
subserved by multiple areas functionally interconnected. Brain computer interfaces (BCIs) may benefit
from feature extraction based on metrics derived from brain connectivity [1]-[3]. However, reaching the
minimally required accuracy when few data samples are available as in single trial or real-time
connectivity applications is still challenging. Variables selection algorithms could represent a valuable
alternative to the classical algorithms, since they provide accurate connectivity estimates even when the
amount of data samples available is very scarce [4]. The aim of the present work is to propose an
accurate and reliable approach for connectivity estimation that paves the way to the use of features
extracted from a connectivity analysis for BCI applications. Methods and Results A. Variable selection
approaches When few data samples are available for multivariate analysis, it is necessary to use
estimators based on variable selection approaches. In this works, we selected LASSO, Group LASSO,
Fused LASSO and Elastic Net algorithms [5]. The idea at the basis of variable selection techniques is to
select only those connectivity parameters that have an effect on the response vector. The remaining
parameters are set to zero. B. Simulation study Algorithms were tested on simulated data (N=50)
according to the following steps: i. Generation of simulated EEG datasets, fitting predefined ground
truth networks of 10 nodes under different condition of factor DL (80, 120, 160, 240, 320, 480), where
DL is the number of data samples available for the estimation process. ii. Estimation of connectivity
parameters by means of variable selection methods (factor TYPE: LASSO, E-NET, F-LASSO, G-LASSO). iii.
Evaluation of their performances by means of MAPE (for parameters estimation) and AUC parameters
(for the validation) [6], [7]. MAPE represents the error committed in estimating the connection strength,
while AUC measures the accuracy in the assessment of null and non-null connections. A repeated
measures ANOVA was computed on MAPE and AUC parameters considering as within factors DL and
TYPE. C. Results As reported in Figure 1, we found that the MAPE parameter (panel a) decreased with
the increasing of the number of data samples available (F(15,735)=47.1, p parameter (panel b) showed the opposite trend (F(15,735)=106.2, p observed with all the algorithms tested, LASSO showed the best performances (lowest MAPE and
highest AUC for all amount of data samples considered). Discussion The presented findings obtained by
applying variable selection approaches demonstrated the possibility to reliably estimate brain
connectivity based on few data samples. Despite the boundary conditions in which all the algorithms
were analysed, they tend to the optimal values of MAPE (0%) and AUC (1) with the increase of number
of data samples available for the estimation process. Noticeably, LASSO regression showed the best
performances for both estimation and validation procedures as compared with other considered
algorithms. Even if variable selection approaches have already been used for brain connectivity