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The objective of the current study was to investigate the anti-epileptogenic and anticonvulsant effects of Dorema ammoniacum gum, which is used in Iranian traditional medicine for the treatment of seizures. Animals received pentylenetetrazol (IP, 30 mg/kg/48 h) for inducing seizures. Five different seizure stages were evaluated for 20 min and parameters including maximum seizure stage, the latency to the onset of stage 4, stage 4 duration, and seizure duration were measured. D. ammoniacum (50 and 100 mg/kg) or its vehicle was administered 30 min before or after pentylenetetrazol injection in different groups. In addition, the effective dose of D. ammoniacum (100 mg/kg) on different seizure stages was compared with the common antiseizure drug phenobarbital. In another set of experiments, we investigated the effective dose of D. ammoniacum on fully kindled animals in which an interictal electroencephalogram was recorded by superficial electrodes placed on the skull. The results showed that D. ammoniacum administration, before and after pentylenetetrazol injections, significantly decreased seizure stage, seizure duration, stage 4 duration, and 1/stage 4 latency. The anti-epileptogenic effect of D. ammoniacum was about 50 to 60% of phenobarbital. In addition, D. ammoniacum significantly decreased seizure stage, seizure duration, stage 4 duration, and 1/stage 4 latency when administered to fully kindled animals but had no effect on the power of EEG sub-bands. These results indicate that D. ammoniacum has anti-epileptogenic and anticonvulsant effects in a chemical kindling model of seizures., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Purpose: To observe age- and sex-related differences in the complexity of the global and hemispheric white matter (WM) throughout adulthood by means of fractal dimension (FD)., Methods: A box-counting algorithm was used to extract FD from the WM magnetic resonance images of 209 healthy adults from three structural layers, including general (gFD), skeleton (sFD), and boundaries (bFD). Model selection algorithms and statistical analyses, respectively, were used to examine the patterns and significance of the changes., Results: gFD and sFD showed inverse U-shape patterns with aging, with a slighter slope of increase from young to mid-age and a steeper decrease to the old. bFD was less affected by age. Sex differences were evident, specifically in gFD and sFD, with men showing higher FDs. Age × sex interaction was significant mainly in the hemispheric analysis, with men undergoing sharper age-related changes. After adjusting for the volume effect, age-related results remained approximately the same, but sex differences changed in most of the features, with women indicating higher values, specifically in the left hemisphere and boundaries. Right hemisphere was still more complex in men., Conclusions: This study is the first that investigates the WM FD spanning adulthood, treating age both as a continuous and categorical variable. We found positive correlations between FD and volume, and our results show similarities with those investigating small-world properties of the brain networks, as well as those of functional complexity and WM integrity. These suggest that FD could yield a highly compact description of the structural changes and also might inform us about functional and cognitive variations.

The aim of this study is to investigate the effect of Quran on a Persian-speaking Muslim. Volunteers listened to three different audio files (Verses from Sura 'Forqan' unconsciously; Arabic text unconsciously; Verses from Sura 'Fath' consciously). EEG signals were recorded and the changes in the relative power of theta and alpha band are considered an indicators of relaxation. The findings indicate that conscious listening to Holy Quran increases the relative theta power in most areas of the head, compared to the rest condition, and listening to Quran unconsciously increased relative theta power in the frontal and central lobes of the head significantly, compared to the rest condition. Also, listening to Quran consciously increases the relative alpha power in the frontal lobe, compared to the rest condition.

This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi-channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Frontalis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed.

This paper presents the design of an adaptable Human-Machine Interface (HMI) for controlling virtual forearm prosthesis. Direct physical performance measures (obtained score and completion time) for the requested tasks were calculated. Furthermore, bioelectric signals from the forehead were recorded using one pair of electrodes placed on the frontal region of the subject head to extract the mental (affective) measures while performing the tasks. By employing the proposed algorithm and above measures, the proposed HMI can adapt itself to the subject's mental states, thus improving the usability of the interface. The quantitative results from 15 subjects show that the proposed HMI achieved better physical performance measures in comparison to a conventional non-adaptive myoelectric controller (p < 0.001).

In the present paper a number of techniques were applied to determine the effects of epileptic seizure on spontaneous ongoing EEG. The idea is that seizure represents transitions of an epileptic brain from its normal (chaotic) state to an abnormal (more ordered) state. Some nonlinear measures including correlation dimension, maximum Lyapunov exponent and wavelet entropy and a graphical tool, named recurrence plot, as well as a novel technique that collects some statistics of the state space organization were used to characterize interictal, preictal and ictal states and derivate a phase transition. The novelty of this work includes of introducing new types of indicators base upon some nonlinear features besides of proposing a new feature of point distribution in phase space. Our results show that (1) these three states are separable in 3-D feature space of nonlinear measures with a gradual decrease of their quantity in seizure evolution, (2) strong rhythmicity, which manifests in recurrence plots and recurrence quantification analysis measures, appears in dynamic while having entered into seizure and (3) different volumes of state space are occupied during each phase of epileptic disorder. The significance of the work is that this information is a step into the detection of a preictal state and consequently is helpful in the prediction and control of epileptic seizures.