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

摘要: Abstract Humans are remarkably efficent at recognizing objects. Understanding how the brain performs object recognition has been challenging. Our understanding advanced substantially in recent years with development of multivariate decoding methods. Most start-of-the-art procedures, make use ‘mean’ neural activation to extract category information, which overlooks temporal variability signals. Here, we studied category-related information 30 mathematically distinct features from electroencephalography (EEG) across three independent and highly-varied datasets using decoding. While event-related potential (ERP) components N1 P2a were among most informative features, original signal samples Wavelet coefficients, selected through principal component analysis, outperformed them. The four mentioned showed more pronounced Theta frequency band, suggested support feed-forward processing visual brain. Correlational analyses that about categories, could predict participants’ behavioral performance (reaction time) accurately than less features. These results suggest a new approach for studying human encodes can read them out optimally investigate dynamics code. codes available online https://osf.io/wbvpn/.