Spatial and frequency characterization of sleep spindles have been extensively addressed using M/EEG or fMRI recordings. However, its intraindividual variability across time has not been addressed. Here we propose to assess the intraindividual variability of sleep spindles in a time-resolved way by means of a trial-to-trial-variability (TTV) measure. For that purpose, the EEG of 26 healthy subjects were recorded overnight. After an exhaustive preprocessing pipeline to remove artifacts, spindles were automatically detected using a complex demodulation-based method. Then, the Wavelet Scalogram was estimated to validate it. Spindle TTV of each participant was also computed for all the conventional EEG frequency bands. Root mean square (RMS) of each TTV signal was calculated as a measure of the total variability of each spindle. Results showed significant differences in the variability between frequencies. Specifically, RMS in the beta-1 frequency band showed higher values as compared to all the other frequency bands (p<0.001). TTV curves showed a dichotomic trend, with lower frequencies showing an increase in the variability before the spindle onset, and higher frequencies showing such increase after the onset. The dependence of the spindle variability with the frequency could be explained by the influence of the multiple cortical generators involved.Clinical Relevance - Sleep spindles are similarly affected in different cognitive-related disorders, which supports the relevance of assessing abnormal sleep patterns as a possible cause for such cognitive deficits.