Title:  Source space decoding and verbal fluency

Abstract: In the first part of my talk, I will talk about a new model for optimal decoding of MEG and EEG oscillatory activity: Our model is as accurate and interpretable as decoding in source space but has considerably lower computational cost. I will share the results of our simulations as well as a real data example.
In the second part of my talk, I will share new results from the intracranial EEG data of a verbal fluency task. We looked at low-frequency power dynamics and connectivity in relation to word retrieval and production. Our analyses revealed that verbal fluency is marked by theta, alpha, and beta power decreases with a high involvement of temporal lobe regions. Interestingly, the connectivity analysis further suggests an involvement of visual areas.