Dissociable components of oscillatory activity underly information encoding in human perception

Abstract

Brain decoding can predict visual perception from non-invasive electrophysiological data by combining information across multiple channels. However, decoding methods typically confound together the multi-faceted and distributed neural processes underlying perception, so it is unclear what specific aspects of the neural computations involved in perception are reflected in this type of macroscale data. Using MEG data recorded while participants viewed a large number of naturalistic images, we analytically separated the brain signal into a slow 1/f drift (<5Hz) and a oscillatory response in the theta frequency band. Combined with a method for capturing between-trial variability in the way stimuli are processed, this analysis revealed that there are at least three dissociable components that contain distinct stimulus-specific information: a 1/f component, reflecting the temporally stable aspect of the stimulus representation; a global phase shift of the theta oscillation, related to differences in the speed of processing between the stimuli; and differential patterns of theta phase across channels, likely related to stimulus-specific computations. We demonstrate that common cognitive interpretations of decoding analysis can be flawed if the multicomponent nature of the signal is ignored, and suggest that, by acknowledging this fact, we can provide a more accurate interpretation of commonly observed phenomena in the study of perception.

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

“…This is due to the fact that the instantaneous phase information contained within the original EEG signal is discarded during the analytic power envelope computation (see Appendix B for further details). This approximation is employed in recent literature [72][77] to remove instantaneous phase from certain brain oscillations and to study how this phase information contributes to decoding performance.…”

“…Therefore, if there is no evidence of temporal generalization, different patterns of activity can be inferred [57]. However, a recent study demonstrated that this interpretation is not always valid, suggesting that this phenomenon can be explained as an artefact of the manner in which the decoding accuracy provided by different components of the signal combine to bring about the overall decoding accuracy [72]. Regardless of the previously selected type of analysis (MVPA or MVCC), the calculation of the temporal generalization matrix can be enabled in the MVPAlab configuration structure as follows:…”

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

“…demonstrated that voltage amplitude and alpha-band power both reliably decoded attention orientation, however alpha-band power was more associated with attention orienting in space while voltage amplitude signaled perceptual processes associated with attention. However, these frequency components must be extracted over a temporal window, thereby resulting in some loss of temporal resolution and increase in the potential dimensionality of the data ( Vidaurre et al, 2020 ).…”

Time-resolved multivariate pattern analysis of infant EEG data

“…However, these frequency components must be extracted over a temporal window, thereby resulting in some loss of temporal resolution and increase in the potential dimensionality of the data (Vidaurre et al, 2020).…”