Dictionary learning for M/EEG multidimensional data

Christos Papageorgakis (1, 2) , Sebastian Hitziger (2) , Théodore Papadopoulo (2)

1 APICS - Analysis and Problems of Inverse type in Control and Signal processing
2 ATHENA - Computational Imaging of the Central Nervous System

Signals obtained from magneto- or electroencephalography (M/EEG) are very noisy and inherently multi-dimensional, i.e. provide a vector of measurements at each single time instant. To cope with noise, researchers traditionally acquire measurements over multiple repetitions (trials) and average them to classify various patterns of activity. This is not optimal because of trial-to-trial variability (waveform variation, jitters). The jitter-adaptive dictionary learning method (JADL) has been developed to better handle for this variability (with a particular emphasis on jitters). JADL is a data-driven method that learns a dictionary (prototype pieces) from a set of signals, but is currently limited to a single channel, which restricts its capacity to work with very noisy data such as M/EEG. We propose an extension to the jitter-adaptive dictionary learning method, that is able to handle multidimensional measurements such as M/EEG.

Domaines: Traitement du signal et de l'image [eess.SP]; Electromagnétisme

Christos Papageorgakis, Sebastian Hitziger, Théodore Papadopoulo. Dictionary learning for M/EEG multidimensional data. International conference on basic and clinical multimodal imaging (BACI), Sep 2015, Utrecht, Netherlands. ⟨hal-01243284⟩