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




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 6 cm 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.

Author(s): Besserve, M. and Martinerie, J. and Garnero, L.
Journal: NeuroImage
Volume: 55
Number (issue): 4
Pages: 1536-1547
Year: 2011
Month: April
Day: 15

Department(s): Empirical Inference
Research Project(s): Learning and inference for Neuroscience
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1016/j.neuroimage.2011.01.056

Links: Web


  title = {Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view},
  author = {Besserve, M. and Martinerie, J. and Garnero, L.},
  journal = {NeuroImage},
  volume = {55},
  number = {4},
  pages = {1536-1547},
  month = apr,
  year = {2011},
  month_numeric = {4}