Predicting the recognition of natural scenes from single trial MEG recordings of brain activity.

In our daily life we look at many scenes. Some are rapidly forgotten, but others we recognize later. We accurately predicted recognition success with natural scene photographs using single trial magnetoencephalography (MEG) measures of brain activation. Specifically, we demonstrate that MEG responses in the initial 600 ms following the onset of scene photographs allow for prediction accuracy rates up to 84.1% using linear Support-Vector-Machine classification (lSVM). A permutation test confirmed that all lSVM based prediction rates were significantly better than "guessing". More generally, we present four approaches to analyzing brain function using lSVMs. (1) We show that lSVMs can be used to extract spatio-temporal patterns of brain activation from MEG-data. (2) We show lSVM classification can demonstrate significant correlations between comparatively early and late processes predictive of scene recognition, indicating dependencies between these processes over time. (3) We use lSVM classification to compare the information content of oscillatory and event-related MEG-activations and show they contain a similar amount of and largely overlapping information. (4) A more detailed analysis of single-trial predictiveness of different frequency bands revealed that theta band activity around 5 Hz allowed for highest prediction rates, and these rates are indistinguishable from those obtained with a full dataset. In sum our results clearly demonstrate that lSVMs can reliably predict natural scene recognition from single trial MEG-activation measures and can be a useful tool for analyzing predictive brain function.