OBJECTIVE: When EEG is recorded in humans, the question arises whether the resting EEG remains stable. We compared the inter-individual variation in spectral observables to the intra-individual stability over more than a year. METHODS: We recorded resting EEG in 55 healthy adults with eyes closed. In 20 persons EEG was recorded in a second session with retest intervals 12-40 months. For electrodes AFz, Cz and Pz alpha peak frequency and alpha peak height were transformed into Z-scores. We compared the curve shape of power spectra by first aligning alpha peaks to 10Hz and then regressing spectra pairwise onto each other to calculate a t-value. The t-value and differences of Z-scores for all pairs of sessions were entered into a generalized linear model (GLM) where binary output represents the recognition probability. The results were cross-validated by out-of-sample testing. RESULTS: Of the 40 sessions, 35 were correctly matched. The shape of power spectra contributed most to recognition. Out of all 2960 pairwise comparisons 99.5% were correct, with sensitivity 88% and specificity 99.5%. CONCLUSIONS: Our statistical apparatus allows to identify those spectral EEG observables which qualify as statistical signature of a person. SIGNIFICANCE: The effect of external factors on EEG observables can be contrasted against their normal variability over time.
OBJECTIVE: When EEG is recorded in humans, the question arises whether the resting EEG remains stable. We compared the inter-individual variation in spectral observables to the intra-individual stability over more than a year. METHODS: We recorded resting EEG in 55 healthy adults with eyes closed. In 20 persons EEG was recorded in a second session with retest intervals 12-40 months. For electrodes AFz, Cz and Pz alpha peak frequency and alpha peak height were transformed into Z-scores. We compared the curve shape of power spectra by first aligning alpha peaks to 10Hz and then regressing spectra pairwise onto each other to calculate a t-value. The t-value and differences of Z-scores for all pairs of sessions were entered into a generalized linear model (GLM) where binary output represents the recognition probability. The results were cross-validated by out-of-sample testing. RESULTS: Of the 40 sessions, 35 were correctly matched. The shape of power spectra contributed most to recognition. Out of all 2960 pairwise comparisons 99.5% were correct, with sensitivity 88% and specificity 99.5%. CONCLUSIONS: Our statistical apparatus allows to identify those spectral EEG observables which qualify as statistical signature of a person. SIGNIFICANCE: The effect of external factors on EEG observables can be contrasted against their normal variability over time.
Authors: Nicolas Langer; Andreas Pedroni; Lorena R R Gianotti; Jürgen Hänggi; Daria Knoch; Lutz Jäncke Journal: Hum Brain Mapp Date: 2011-05-09 Impact factor: 5.038
Authors: Craig E Tenke; Jürgen Kayser; Jorge E Alvarenga; Karen S Abraham; Virginia Warner; Ardesheer Talati; Myrna M Weissman; Gerard E Bruder Journal: Clin Neurophysiol Date: 2018-04-16 Impact factor: 3.708