  Recently, there has been growing interest in the analysis of spectrograms of
ElectroEncephaloGram (EEG), particularly to study the neural correlates of
(un)-consciousness during General Anesthesia (GA). Indeed, it has been shown
that order three tensors (channels x frequencies x times) are a natural and
useful representation of these signals. However this encoding entails
significant difficulties, especially for convolutional sparse coding (CSC) as
existing methods do not take advantage of the particularities of tensor
representation, such as rank structures, and are vulnerable to the high level
of noise and perturbations that are inherent to EEG during medical acts. To
address this issue, in this paper we introduce a new CSC model, named Kruskal
CSC (K-CSC), that uses the Kruskal decomposition of the activation tensors to
leverage the intrinsic low rank nature of these representations in order to
extract relevant and interpretable encodings. Our main contribution, TC-FISTA,
uses multiple tools to efficiently solve the resulting optimization problem
despite the increasing complexity induced by the tensor representation. We then
evaluate TC-FISTA on both synthetic dataset and real EEG recorded during GA.
The results show that TC-FISTA is robust to noise and perturbations, resulting
in accurate, sparse and interpretable encoding of the signals.
