Journal article

Publication year: 2008

Pages: 1056-1068

Journal: NeuroImage

Volume number: 42

Issue number: 3

ISSN: 1053-8119

eISSN: 1095-9572

DOI Link: https://doi.org/10.1016/j.neuroimage.2008.06.014

Publisher: Elsevier

Abstract: 
In our daily life we took 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 (ISVM). A permutation test confirmed that all ISVM based prediction rates were significantly better than "guessing". More generally, we present four approaches to analyzing brain function using ISVMs. (1) We show that ISVMs can be used to extract spatio-temporal patterns of brain activation from MEG-data. (2) We show ISVM 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 ISVM 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 ISVMs can reliably predict natural scene recognition from single trial MEG-activation measures and can be a useful tool for analyzing predictive brain function. (c) 2008 Elsevier Inc. All rights reserved.

Harvard Citation style: Rieger, J., Reichert, C., Gegenfurtner, K., Noesselt, T., Braun, C., Heinze, H., et al. (2008) Predicting the recognition of natural scenes from single trial MEG recordings of brain activity, NeuroImage, 42(3), pp. 1056-1068. https://doi.org/10.1016/j.neuroimage.2008.06.014

APA Citation style: Rieger, J., Reichert, C., Gegenfurtner, K., Noesselt, T., Braun, C., Heinze, H., Kruse, R., & Hinrichs, H. (2008). Predicting the recognition of natural scenes from single trial MEG recordings of brain activity. NeuroImage. 42(3), 1056-1068. https://doi.org/10.1016/j.neuroimage.2008.06.014