Brain Connectivity & Machine Learning

Causal connectivity analysis of tracking faces in presence of repeated distractors during working memory tasks

J.M. Cortes, D. Marinazzo, P. Tudela and E. Madrid. Causal connectivity analysis of tracking faces in presence of repeated distractors during working memory tasks. Society for Neuroscience Annual Meeting 2009
Recognition of a specific visual target among equally familiar distracters requires neural mechanisms for tracking items in working memory. High Density EEG recordings were obtained from sixteen right-handed normal participants while they performed a visual working memory task in which familiarity for targets and distracters was similar. Each trial consisted of a target face to be remembered, presented for 3.5 s, followed by 13 faces presented at a rate of 1.5 s per face. The target and one of the distracters were repeated up to five times in a given trial, separated by 3 to 15 s. Granger causality states that a given signal A has causal influence on B if the predictability on B improves adding to B the past of A. We have applied Granger connectivity analysis (Seth 2005) to study the dynamics of the neural networks involved on the recognition of a visual target stimulus among equally familiar distracter, obtaining the causal interactions among the evenly spaced array of 128 EEG electrodes on the scalp. By using Kernel methods (Marinazzo et al 2008), we extended the results in (Seth 2005) to achieve detection of non-linear causal relationships. Causal connectivity analysis is a new tool to look at EEG data and it allows for establishing new correlates among electrical brain activity and cognitive processes.

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