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Decoding sequence learning from single-trial intracranial EEG in humans

Contributors

De Lucia, Marzia; Constantinescu, Irina Oana; Sterpenich, Virginie; Pourtois, Gilles; Seeck, Margitta; Schwartz, Sophie

Published in

PloS one, vol. 6, no. 12, e28630

Publication date

2011

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

  • Adult
  • Algorithms
  • Behavior/physiology
  • Brain/physiology
  • Electroencephalography/classification/methods
  • Female
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Learning/physiology
  • Male
  • Markov Chains
  • Multivariate Analysis

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

DE LUCIA, Marzia et al. Decoding sequence learning from single-trial intracranial EEG in humans. In: PloS one, 2011, vol. 6, n° 12, p. e28630. doi: 10.1371/journal.pone.0028630

Main files (1)

Article (Published version)
CC BY

Identifiers

Journal ISSN

1932-6203
780views
409downloads

Technical informations

Creation

19/11/2012 12:54:00

First validation

19/11/2012 12:54:00

Update

14/03/2023 19:57:58

Status update

14/03/2023 19:57:58

Last indexation

08/11/2024 11:12:27
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