The best of two worlds: Decoding and source-reconstructing M/EEG oscillatory activity with a unique model
Westner, B. U.; King, J.-R.
Show abstract
The application of decoding models to electrophysiological data has become standard practice in neuroscience. The use of such methods on sensor space data can, however, limit the interpretability of the results, since brain sources cannot be readily estimated from the decoding of sensor space responses. Here, we propose a new method that combines the common spatial patterns (CSP) algorithm with beamformer source reconstruction for the decoding of oscillatory activity. We compare this method to sensor and source space decoding and show that it performs equally well as source space decoding with respect to both decoding accuracy and source localization without the extensive computational cost. We confirm our simulation results on a real MEG data set. In conclusion, our proposed method performs as good as source space decoding, is highly interpretable in the spatial domain, and has low computational cost.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.