Ea 481 Laboratoire Of Neurosciences Of Besancon

Besançon, France

Ea 481 Laboratoire Of Neurosciences Of Besancon

Besançon, France

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Henriques J.,Laboratoire Of Mathematiques Of Besancon | Gabriel D.,Besancon University Hospital Center | Gabriel D.,Ea 481 Laboratoire Of Neurosciences Of Besancon | Grigoryeva L.,Laboratoire Of Mathematiques Of Besancon | And 8 more authors.
Clinical EEG and Neuroscience | Year: 2016

Recent studies have evidenced serious difficulties in detecting covert awareness with electroencephalography-based techniques both in unresponsive patients and in healthy control subjects. This work reproduces the protocol design in two recent mental imagery studies with a larger group comprising 20 healthy volunteers. The main goal is assessing if modifications in the signal extraction techniques, training-testing/cross-validation routines, and hypotheses evoked in the statistical analysis, can provide solutions to the serious difficulties documented in the literature. The lack of robustness in the results advises for further search of alternative protocols more suitable for machine learning classification and of better performing signal treatment techniques. Specific recommendations are made using the findings in this work. © EEG and Clinical Neuroscience Society.


Henriques J.,Laboratoire Of Mathematiques Of Besancon | Pazart L.,Besancon University Hospital Center | Pazart L.,Ea 481 Laboratoire Of Neurosciences Of Besancon | Grigoryeva L.,Laboratoire Of Mathematiques Of Besancon | And 12 more authors.
PLoS ONE | Year: 2016

To measure the level of residual cognitive function in patients with disorders of consciousness, the use of electrophysiological and neuroimaging protocols of increasing complexity is recommended. This work presents an EEG-based method capable of assessing at an individual level the integrity of the auditory cortex at the bedside of patients and can be seen as the first cortical stage of this hierarchical approach. The method is based on two features: first, the possibility of automatically detecting the presence of a N100 wave and second, in showing evidence of frequency processing in the auditory cortex with a machine learning based classification of the EEG signals associated with different frequencies and auditory stimulation modalities. In the control group of twelve healthy volunteers, cortical frequency processing was clearly demonstrated. EEG recordings from two patients with disorders of consciousness showed evidence of partially preserved cortical processing in the first patient and none in the second patient. From these results, it appears that the classification method presented here reliably detects signal differences in the encoding of frequencies and is a useful tool in the evaluation of the integrity of the auditory cortex. Even though the classification method presented in this work was designed for patients with disorders of consciousness, it can also be applied to other pathological populations. © 2016 Henriques et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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