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 1.Bandpass filtering (different gamma bands) 2.Construct time-delay embedding vector for each dipole 3.Detect recurrent states using autocorrelation integral 4.Construct binary vector that denotes recurrent states 5.Classifier training on 180/240 trials 6.Four-fold cross-validation Stats: Bootstrap estimation (permutation testing); Bonferroni correction

5. Results Classifier performance in left pFC during 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