{'fraction_non_alphanumeric': 0.04190999656475438, 'fraction_numerical': 0.017519752662315355, 'mean_word_length': 4.75494071146245, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
[The use of electrophysiological techniques to project typical psychotropic drug effects: some examples (author's transl)].

A long existing hypothesis, i.e. that the EEG effects, in lead O2A2, of typical psychotropic drugs are substance class specific, when given as single oral dosages to healthy volunteers, is discussed. Using the technique of pharmacoelectroencephalograhpy, five typical representatives of neuroleptics, anxiolytics, antidepressives, psychostimulants (Fig. 2) as well as placebo were investigated in 75 volunteers, each receiving one representative of each substance class in a double blind 5-fach change over latin square design (Fig. 3). A five minute EEG record for lead O2A2 was obtained pre, 1 h and 3 hrs post drug intake unter RR (Relaxed Recording) conditions. Typical samples of th EEG records are shown in Figs. 4--7. Parametrisation was done using power spectrum analysis. Then 3 x 7 target variables were formed--six relative power values in predetermined frequency bands and the total power between 1.5 and 30.0 Hz for 3 occasions of measurement (Fig. 4--7). Using a (five-group) linear discriminant analysis the substance effects on the EEG were transformed into 5 probability measures for the five substance classes (Fig. 4--7). The present paper should provide a demonstration of some typical examples, using single subjects, of the projection of substance effects onto an electrophysiological level using a vector of 21 components (7 target variables for 3 occasions of measurement) as can be seen from Table 1. Furthermore, the transformation into probability measures of the five substance classes are shown in Table 2. Table 3 shows five examples of projections of substance effects, which do not fit into the classifications to which they belong. An attempt is made to explain, whether single target variables from the power spectrum can contribute differently to the discrimination between single substances of different substance classes. Within the accepted system of 4 psychotropic drug classes, the following variables seem to be of importance: a) The benzodiazepine anxiolytics show a marked increase in beta F1 (12.0--18.0 Hz) power and activity and, related to sedation, an increase in delta F-power and a decrease in alpha-power; b) The psychostimulants of the amphetamine type show an increase in total power and alpha-power, an increase in the power of the frequency ranges near to the alpha-band (slow beta, fast theta) and a decrease in delta F (1.5--5.5 Hz)-power, when delta F-power is high in the pre-values, indicating a stabilization in vigilance; c) The neuroleptics show a marked increase in theta F (5.5--8.5 Hz)-power, some increase in the delta F--and a decrease in the beta F1-and beta F3-power; d) Tricyclic antidepressants show an interaction between delta F-theta F-, alpha- and beta-power, in the sense of a dissociative shift in vigilance.