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Decoding sequence learning from single-trial intracranial EEG in humans

(2011) PLOS ONE. 6(12).
Author
Organization
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The integrative neuroscience of behavioral control (Neuroscience)
Abstract
We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.
Keywords
BRAIN-COMPUTER INTERFACES, HIDDEN MARKOV-MODELS, PREFRONTAL CORTEX, MICROSTATE DURATION, PATTERN-ANALYSIS, MEMORY, SLEEP, HIPPOCAMPUS, CLASSIFICATION, DYNAMICS

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Citation

Please use this url to cite or link to this publication:

Chicago
De Lucia, Marzia, Irina Constantinescu, Virginie Sterpenich, Gilles Pourtois, Margitta Seeck, and Sophie Schwartz. 2011. “Decoding Sequence Learning from Single-trial Intracranial EEG in Humans.” Plos One 6 (12).
APA
De Lucia, M., Constantinescu, I., Sterpenich, V., Pourtois, G., Seeck, M., & Schwartz, S. (2011). Decoding sequence learning from single-trial intracranial EEG in humans. PLOS ONE, 6(12).
Vancouver
1.
De Lucia M, Constantinescu I, Sterpenich V, Pourtois G, Seeck M, Schwartz S. Decoding sequence learning from single-trial intracranial EEG in humans. PLOS ONE. 2011;6(12).
MLA
De Lucia, Marzia, Irina Constantinescu, Virginie Sterpenich, et al. “Decoding Sequence Learning from Single-trial Intracranial EEG in Humans.” PLOS ONE 6.12 (2011): n. pag. Print.
@article{2035029,
  abstract     = {We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.},
  articleno    = {e28630},
  author       = {De Lucia, Marzia and Constantinescu, Irina and Sterpenich, Virginie and Pourtois, Gilles and Seeck, Margitta and Schwartz, Sophie},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {BRAIN-COMPUTER INTERFACES,HIDDEN MARKOV-MODELS,PREFRONTAL CORTEX,MICROSTATE DURATION,PATTERN-ANALYSIS,MEMORY,SLEEP,HIPPOCAMPUS,CLASSIFICATION,DYNAMICS},
  language     = {eng},
  number       = {12},
  pages        = {11},
  title        = {Decoding sequence learning from single-trial intracranial EEG in humans},
  url          = {http://dx.doi.org/10.1371/journal.pone.0028630},
  volume       = {6},
  year         = {2011},
}

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