Skip to Main content Skip to Navigation
Journal articles

Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view.

Abstract : Decoding experimental conditions from single trial Electroencephalographic (EEG) signals is becoming a major challenge for the study of brain function and real-time applications such as Brain Computer Interface. EEG source reconstruction offers principled ways to estimate the cortical activities from EEG signals. But to what extent it can enhance informative brain signals in single trial has not been addressed in a general setting. We tested this using the minimum norm estimate solution (MNE) to estimate spectral power and coherence features at the cortical level. With a fast implementation, we computed a support vector machine (SVM) classifier output from these quantities in real-time, without prior on the relevant functional networks. We applied this approach to single trial decoding of ongoing mental imagery tasks using EEG data recorded in 5 subjects. Our results show that reconstructing the underlying cortical network dynamics significantly outperforms a usual electrode level approach in terms of information transfer and also reduces redundancy between coherence and power features, supporting a decrease of volume conduction effects. Additionally, the classifier coefficients reflect the most informative features of network activity, showing an important contribution of localized motor and sensory brain areas, and of coherence between areas up to 6cm distance. This study provides a computationally efficient and interpretable strategy to extract information from functional networks at the cortical level in single trial. Moreover, this sets a general framework to evaluate the performance of EEG source reconstruction methods by their decoding abilities.
Document type :
Journal articles
Complete list of metadata

https://hal.inria.fr/hal-00795718
Contributor : Olivier Colliot <>
Submitted on : Thursday, February 28, 2013 - 6:14:57 PM
Last modification on : Thursday, December 10, 2020 - 3:38:54 AM

Identifiers

Citation

Michel Besserve, Jacques Martinerie, Line Garnero. Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view.. NeuroImage, Elsevier, 2011, 55 (4), pp.1536-1547. ⟨10.1016/j.neuroimage.2011.01.056⟩. ⟨hal-00795718⟩

Share

Metrics

Record views

352