Assessing EEG sleep spindle propagation. Part 1: Theory and proposed methodology - O’Reilly, Christian; Nielsen, Tore (Journal of neuroscience methods, 2014)
(a) Assessment of propagation is achieved by cross-correlating between-channel time-frequency representations of EEG sleep spindles. (b) Example of results showing average propagation delays between adjacent electrodes of the 10–20 system. •A novel algorithm to evaluate EEG sleep spindle propagation is presented.•Implementation is available as an open source Python library entitled Spyndle.•Patterns of observed propagation are repeatable across subjects.•The proposed methodology could be applied to other transient EEG/MEG events. A convergence of studies has revealed sleep spindles to be associated with sleep-related cognitive processing and even with fundamental waking state capacities such as intelligence. However, some spindle characteristics, such as propagation direction and delay, may play a decisive role but are only infrequently investigated because of technical complexities. A new methodology for assessing sleep spindle propagation over the human scalp using noninvasive electroencephalography (EEG) is described. This approach is based on the alignment of time-frequency representations of spindle activity across recording channels. This first of a two-part series concentrates on framing theoretical considerations related to EEG spindle propagation and on detailing the methodology. A short example application is provided that illustrates the repeatability of results obtained with the new propagation measure in a sample of 32 night recordings. A more comprehensive experimental investigation is presented in part two of the series. Compared to existing methods, this approach is particularly well adapted for studying the propagation of sleep spindles because it estimates time delays rather than phase synchrony and it computes propagation properties for every individual spindle with windows adjusted to the specific spindle duration. The proposed methodology is effective in tracking the propagation of spindles across the scalp and may thus help in elucidating the temporal aspects of sleep spindle dynamics, as well as other transient EEG and MEG events. A software implementation (the Spyndle Python package) is provided as open source software.