No abstract

An automatic method for classification of EEG data, based upon segmentation of the signal using the autoregressive model and decision making in fuzzy environment, is described. The classification is applied to explore the relations between EEG states during waking, and vigilance performance studied through auditory choice reaction time. The average auditory choice reaction time measured during occurrences of "alpha" segments was significantly shorter than that measured during occurrences of "nonalpha" signal segments. A significant negative correlation was also found between the segments auditory choice reaction time and the segments spectral power in the alpha or beta frequency band.

Computerized analysis of polygraphic sleep recordings was carried out for the evaluation of midazolam, a benzodiazepine hypnotic. The analysis was carried out in real time on a small laboratory computer, and the output included the hypnogram and relative power profiles for the main electroencephalogram activities. Analysis showed a slight "intranight rapid eye movement rebound" during medication and reduction of sleep stage IV after withdrawal. The relative power of the delta frequency band did not change during medication or withdrawal.

Polygraphic sleep recordings during 12 nights in 5 healthy volunteers were classified manually into waking and the various sleep stages. The smoothed power spectra of EEG signal segments defined as waking or one of the sleep stages were calculated via segmentation of the EEG signal, using the autoregressive model, and time-dependent fuzzy clustering. The spectra were derived from the prediction coefficients of the segments. The relative power in the delta frequency band was found to increase monotonically with increasing depth of sleep, together with a parallel decrease in the alpha relative power. In most cases alpha relative power had a small peak during REM sleep, and on average the relative power in the sigma frequency band during REM sleep was smaller than the beta relative power. The power spectra from subjects with no waking alpha differed from those of subjects with abundant waking alpha mostly in the relative spectral content of stages 1 and REM. The significance of these findings is discussed in relation to future standardisation of automatic analysis of sleep recordings.

One of the most important components of the basic microteaching model in teacher education is the feedback obtained from supervisors, learners, peers and from technical aids such as audio and video recordings (Allen and Ryan, 1969). The feedback serves a dual purpose: first it provides the trainee with information regarding his behavior enabling him to design behavioral changes; and secondly, it facilitates the process of self-confrontation by triggering a cognitive dissonance which stimulates the psychological climate conducive to change (Festinger, 1957; Nielsen, 1962;Kagan et al., 1967;Geerstma andMackie, 1969 andOnder, 1970).The use of video recordings in microteaching training provides instant and accurate feedback of verbal and non-verbal classroom interaction. However, an intuitive subjective analysis of the video recording performed by a supervisor, peer or the student-teacher himself, faces the danger of being diffused and distorted by individual biases.The combined use of microteaching with systematic observation instruments for analyzing classroom interaction has been recommended by researchers and practitioners alike (Amidon and Rosenshine, 1968 andMinnis, 1968). Both Allen, who played a major role in the development of microteaching (Allen and Ryan, 1969), and Flanders, who developed one of the most common interaction analysis systems (Flanders, 1970), recommend the combination of their systems as an effective procedure in teacher education. The researcher and practitioner in this area are faced with the question of either using one of the existing instruments or constructing a new one.