Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l1 norm, that can reduce collinearity by means of variable selection process. However, the direct application of l1 norm during the training of an ANN does not result in an efficient learning. With the introduction of the stochastic gradient descent-L1 (SGD-L1) it is possible to apply l1 norm directly on the estimated weights in an efficient way. Even if ANNs has been used as MVAR model for brain connectivity estimation, the use of SGD-L1 algorithm has never been tested to this purpose when few data samples are available. In this work, we tested an approach based on ANNs and SGD-L1 on both surrogate and real EEG data. Our results show that ANNs can provide accurate brain connectivity estimation if trained with SGD-L1 algorithm even when few data samples are available.
Estimation of brain connectivity through Artificial Neural Networks / Antonacci, Yuri; Toppi, Jlenia; Mattia, Donatella; Pietrabissa, Antonio; Astolfi, Laura. - (2019), pp. 636-639. (Intervento presentato al convegno 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) tenutosi a Berlin; Germany) [10.1109/EMBC.2019.8856585].
Estimation of brain connectivity through Artificial Neural Networks
Yuri Antonacci
;jlenia toppi
;Donatella mattia
;antonio pietrabissa
;laura astolfi
2019
Abstract
Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l1 norm, that can reduce collinearity by means of variable selection process. However, the direct application of l1 norm during the training of an ANN does not result in an efficient learning. With the introduction of the stochastic gradient descent-L1 (SGD-L1) it is possible to apply l1 norm directly on the estimated weights in an efficient way. Even if ANNs has been used as MVAR model for brain connectivity estimation, the use of SGD-L1 algorithm has never been tested to this purpose when few data samples are available. In this work, we tested an approach based on ANNs and SGD-L1 on both surrogate and real EEG data. Our results show that ANNs can provide accurate brain connectivity estimation if trained with SGD-L1 algorithm even when few data samples are available.File | Dimensione | Formato | |
---|---|---|---|
Antonacci_Postprint_Estimation-of-brain_2019.pdf
accesso aperto
Note: https://ieeexplore.ieee.org/document/8856585
Tipologia:
Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
376.86 kB
Formato
Adobe PDF
|
376.86 kB | Adobe PDF | |
Antonacci_Estimation-of-brain_2019.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
780.65 kB
Formato
Adobe PDF
|
780.65 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.