Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

Abstract

AbstractHow does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly use the “mean” neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability which is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional “mean” activity. Here we compare the information content of 31 variability-sensitive features against the “mean” of activity, using three independent highly-varied datasets. In whole-trial decoding, the classical event-related potential (ERP) components of “P2a” and “P2b” provided information comparable to those provided by “Original Magnitude Data (OMD)” and “Wavelet Coefficients (WC)”, the two most informative variability-sensitive features. In time-resolved decoding, the “OMD” and “WC” outperformed all the other features (including “mean”), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in Theta frequency band, previously suggested to support feed-forward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously e.g. phase, amplitude and frequency rather than “mean” per se. In our active categorization dataset, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.