Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time-frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time-frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time-frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time-frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.

The study investigates information processing operations in rapid eye movement (REM) sleep and stages of non-REM sleep. An eclectic approach was used in the study whereby the effect of external auditory stimuli was investigated on both the peaks in the event-related potentials (ERP) waveform and on the oscillatory responses that contribute to the morphology of this waveform. Data on overnight sleep were acquired from 12 healthy, young adult, volunteer men; those on the awake stage were obtained from 21 matched men. Brain activity was obtained in response to auditory stimuli (2000 Hz deviant and 1000 Hz standard stimuli: 65 dB, 10 msec r/f time, 50 msec duration) under the passive oddball paradigm in sleep and the active and passive oddball paradigms in wakefulness. The effect of the experimental variables (stimulus type, sleep stage) on ERP peak amplitudes was studied through analysis of variance for repeated measures. The contribution of the oscillatory responses to the ERP peaks was studied using stepwise multiple regression. As represented with the amplitude of the ERP peaks and the oscillatory responses, auditory information processing selectively varied in different stages of sleep. Processing took longer in sleep: comparable peaks were obtained at longer latencies and later components appeared that did not exist in wakefulness. With a long-duration theta activity and greater differentiation between the deviant-and standard-elicited stimuli, Stage 2 appeared to represent effortful cognitive processing. As represented with only the earlier peaks and the insignificant delta activity, REM represented less extended cognitive processing.

The aim was to investigate whether gender is a causative factor in the gamma status according to which some individuals respond with time-locked, early gamma response, G+, while the others do not show this response, G-. The sample consisted of 42 volunteer participants (between 19 and 37 years of age with at least 9 years of education). There were 22 females and 20 males. Data were collected under the oddball paradigm. Auditory stimulation (10 ms r/f time, 50 ms duration, 65 dB SPL) consisted of target (2000 Hz; p = .20) stimuli that occurred randomly within a series of standard stimuli (1000 Hz; p = .80). Gamma responses were studied in the amplitude frequency characteristics, in the digitally filtered event-related potentials (f-ERPs) and in the distributions which were obtained using the recently developed time-frequency component analysis (TFCA) technique. Participants were classified into G+ and G- groups with a criterion of full agreement between the results of an automated gamma detection technique and expert opinion. The 2 x 2 x 2 ANOVA on f-ERPs and 2 x 2 x 2 multivariate ANOVA on TFCA distributions showed the main effect of gamma status and gender as significant, and the interaction between gamma status and gender as nonsignificant. Accordingly, individual difference in gamma status is a reliable phenomenon, but this does not depend on gender. There are conflicting findings in the literature concerning the effect of gender on ERP components (N100, P300). The present study showed that if the gamma status is not included in research designs, it may produce a confounding effect on ERP parameters.

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