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Aim: Decode working memory content from human EEG recordings

Distributed representations reading club presentation by Alexander Backus. Aim: Decode working memory content from human EEG recordings. Methods. Modified delayed match-to-sample (DMS) task. Methods. Mean EEG activity in visual cortex. Methods. Nonlinear signal analysis

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Aim: Decode working memory content from human EEG recordings

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  1. Distributed representations reading club presentation by Alexander Backus Aim: Decode working memory content from human EEG recordings

  2. Methods Modified delayed match-to-sample (DMS) task

  3. Methods Mean EEG activity in visual cortex

  4. Methods • Nonlinear signal analysis • Assumption: State of the dynamical system (e.g. epoch of a given dipole) at any given moment may be represented by an embedding vector, where recurrent states are represented by similar embedding vectors • Bandpassfiltering (different gamma bands) • Construct time-delay embedding vector for each dipole • Detect recurrent states using autocorrelation integral • Construct binary vector that denotes recurrent states • Classifier training on 180/240 trials • Four-fold cross-validation • Stats: Bootstrapestimation(permutation testing); Bonferroni correction

  5. Results Classifier performance in left pFCduring encoding 100-200 Hz 60-100 Hz 30-60 Hz

  6. Results Classifier performance during WM maintenance

  7. Results Cross-frequency analysis Theta-gamma phase-amplitude coupling

  8. Discussion • Synchronous firing in gamma band in pFC during working memory maintenance is stimulus specific • Support for gamma feature-binding hypothesis • Potentially useful for brain-computer interfacing

  9. Thanks for your attention Questions or remarks?

  10. Results

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