Brain signatures of surprise in EEG and MEG data

The brain is constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from predictions shaped by recent trends, neural signals are generated to report this surprise. Existing models for quantifying surprise are based on an ideal observer assumption operating under one of the three definitions of surprise set forth as the Shannon, Bayesian, and Confidence-corrected surprise. In this paper, we analyze both visual and auditory EEG and auditory MEG signals recorded during oddball tasks to examine which temporal components in these signals are sufficient to decode the brain’s surprise based on each of these three definitions. We found that for both recording systems the Shannon surprise is always significantly better decoded than the Bayesian surprise regardless of the sensory modality and the selected temporal features used for decoding. Author summary A regression model is proposed for decoding the level of the brain’s surprise in response to sensory sequences using selected temporal components of recorded EEG and MEG data. Three surprise quantification definitions (Shannon, Bayesian, and Confidence-corrected surprise) are compared in offering decoding power. Four different regimes for selecting temporal samples of EEG and MEG data are used to evaluate which part of the recorded data may contain signatures that represent the brain’s surprise in terms of offering a high decoding power. We found that both the middle and late components of the EEG response offer strong decoding power for surprise while the early components are significantly weaker in decoding surprise. In the MEG response, we found that the middle components have the highest decoding power while the late components offer moderate decoding powers. When using a single temporal sample for decoding surprise, samples of the middle segment possess the highest decoding power. Shannon surprise is always better decoded than the other definitions of surprise for all the four temporal feature selection regimes. Similar superiority for Shannon surprise is observed for the EEG and MEG data across the entire range of temporal sample regimes used in our analysis.