Dictionary Learning for Multidimensional Data - Archive ouverte HAL Access content directly
Conference Papers Year :

Dictionary Learning for Multidimensional Data

(1, 2, 3) , (2, 3) , (2, 3)


Electroencephalography(EEG) and magnetoencephalography (MEG) measure the electrical activity of the functioning brain using a set of sensors placed on the scalp (electrodes and magnetometers). Magneto- or electroencephalography (M/EEG) have the same biological origin, the activity of the pyramidal neurones within the cortex. The signals obtained from M/EEG are very noisy and inherently multi-dimensional, i.e. provide a vector of measurements at each single time instant. To cope with the 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. The jitter-adaptive dictionary learning method (JADL) [1] has been developed to better handle for this variability. JADL is a data-based method that learns a dictionary from a set of signals, but is currently limited to a single channel, which restricts its capacity with very noisy data such as M/EEG. In this paper, we propose an extension to the jitter-adaptive dictionary learning method, in order to handle multidimensional measurements such as M/EEG. A modified model is developed and tested using synthetically generated data set as well as real M/EEG signals. The results obtained using our model look promising, and show superior performance compared to the original single-channel JADL framework.
Fichier principal
Vignette du fichier
DL_for_multidimensional_data_GRETSI2017.pdf (452.71 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01575263 , version 1 (18-08-2017)


Attribution - CC BY 4.0


  • HAL Id : hal-01575263 , version 1


Christos Papageorgakis, Sebastian Hitziger, Théodore Papadopoulo. Dictionary Learning for Multidimensional Data. Proceedings of GRETSI 2017, Sep 2017, Juan-les-Pins, France. ⟨hal-01575263⟩
203 View
186 Download


Gmail Facebook Twitter LinkedIn More