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

• 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 dipole3. Detect recurrent states using autocorrelation integral4. Construct binary vector that denotes recurrent states5. Classifier training on 180/240 trials6. Four-fold cross-validation

• Stats: Bootstrap estimation (permutation testing); Bonferroni

correction

Results

Classifier performance in left pFC during encoding

100-200 Hz60-100 Hz30-60 Hz

Results

Classifier performance during WM maintenance

Results

Cross-frequency analysis

Theta-gamma phase-amplitude coupling

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

Thanks for your attention

Questions or remarks?

Results