Nataliia Kozhemiako & Shaun Purcell We recently used NSRR data to study the so-called spectral slope of the electroencephalogram (EEG), during wake, NREM and REM sleep. This work (described in a bioRxiv pre-print, Kozhemiako et al.) points to the power of NSRR data: thousands of polysomnographic studies that can be rapidly and freely repurposed to address a broad array of research questions. Keep reading
To paraphrase the adage, a picture is worth a thousand numbers. In order to investigate some basic properties of NSRR datasets, here we generate a number of whole-dataset visualizations. To make sense of these images, we’ll employ a remarkably complex computational pattern recognition and dimension reduction framework, a.k.a. the human visual system. Keep reading
Visualizing data is almost always useful, but this can be difficult when you have a lot of complex data. This is where dimension reduction techniques can play an important role. As described here, we applied one such technique to sleep EEG spectra from over 16,000 individuals in the NSRR, to get a feel for some of the sources of individual differences (both physiological and artefactual) in these data. Keep reading
Many analyses of sleep EEG data are effectively agnostic to the polarity of the EEG signal. That is, you could flip the signal (i.e. multiply every sample value by -1) and still obtain equivalent results, e.g. from most spectral analyses. For certain analyses that consider the phase of a signal, however, it will in fact matter that the polarity of the signal is correct. Keep reading