Discrete brain sections were obtained from rats once or repeatedly (once a day for 5 days) given i.p. nifedipine, verapamil or diltiazem at doses ranging from 5 to 20 mg/kg. The biogenic amines and metabolites in the hypothalamus, brain stem, hippocampus, striatum, cortex and thalamus-midbrain were determined by high performance liquid chromatography with electrochemical detection. The drug-induced changes, displaying regional specificity and differences according to the various com pounds, suggested that: (a) serotonergic systems were activated, especially in fasted rats or after repeated treatment; (b) the dopaminergic system of the striatum was inhibited by nifedipine which did reduce the HVA levels and the HVA/DA, as well as the DOPAC/DA, ratio. These effects disappeared after repeated treatment. It was also speculated that the data obtained could be of great interest in view of the possi ble use of calcium antagonists to treat disorders of the central nervous system.

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The complexity of epilepsy created a fertile ground for further research in automated methods, attempting to help the epileptologists’ task. Over the past years, great breakthroughs have emerged in computer-aided analysis and the advent of Brain Computer Interface (BCI) systems has greatly facilitated the automated seizure analysis. In this study, an evaluation of the window size in automated seizure detection is proposed. The EEG signals from the University of Bonn was employed and segmented into 24 epochs of different window lengths with 50% overlap each time. Statistical and spectral features were extracted in the OpenViBE scenario that were used to train four different classifiers. Results in terms of accuracy were above 80% for the Decision Trees classifier. Also, results indicated that different window sizes provide small variations in classification accuracy.

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