Characterization of the cerebral activity by time–frequency representation of evoked EEG potentials

Abstract

Event-related brain potentials (ERPs) are the electrical response of the brain while performing a particular task. Methods traditionally used to study ERPs measure the amplitude and duration of the waveform in order to quantify the changes, being signal morphology dependent. However, the frequency characteristics of those events remain uncovered. The aim of this work was the study of new measures to characterize, by means of time–frequency representation (TFR) techniques, the ERPs recorded while subjects conducted a choice reaction time task (Ericksen flanker task) following the administration of different alprazolam doses. Several measures defined from energy, instantaneous frequency and group delay functions were obtained by means of TFR techniques applied to the Choi–Williams distribution (CWD) of EEG signals. These measures, which are signal morphology independent, were studied in four frequency bands, δ (0–4 Hz), θ (4–8 Hz), α (8–15 Hz), β (15–30 Hz), and for certain time periods. Based on these measures, differences between ERPs were analyzed by comparing the different response types (successes or successfully corrected failures) of the subject performing the task, and comparing the applied drug doses. For each subject, the CWD of EEG signals was applied in two different ways: (a) all ERPs were averaged per channel, and then the CWD was applied; (b) the CWD was applied to each one of the ERPs. When the CWD was applied to each ERP, the energy measures in the δ, θ and β bands, the instantaneous frequency measures in the α and β bands, and the group delay measures in the δ, θ and α bands showed a statistically significant level p < 0.0005 in the analysis of the response type. Also, the energy measures in the θ and β bands and the instantaneous frequency measures in the α band showed statistically significant differences (p < 0.0005) between placebo and low and high drug doses. In contrast, poor results were obtained when all epochs of each subject were averaged per channel. Finally, it was concluded that these results showed that the new proposed measures based on the energy offered a new and more robust way to characterize ERP signals.