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 process. 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.

Antonacci Y., Toppi J., Mattia D., Pietrabissa A., & Astolfi L. (2019). Estimation of brain connectivity through Artificial Neural Networks. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 636-639). Institute of Electrical and Electronics Engineers Inc. [10.1109/EMBC.2019.8856585].

Estimation of brain connectivity through Artificial Neural Networks

Antonacci Y.;
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 process. 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.
Algorithms, Brain, Electroencephalography, Regression Analysis, Neural Networks, Computer
1557170X
Antonacci Y., Toppi J., Mattia D., Pietrabissa A., & Astolfi L. (2019). Estimation of brain connectivity through Artificial Neural Networks. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 636-639). Institute of Electrical and Electronics Engineers Inc. [10.1109/EMBC.2019.8856585].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/428177
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