Aim: Decode working memory content from human EEG recordings
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Transcript of 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
• 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