Uniform decrease of alpha-global field power induced by intermittent photic stimulation of healthy subjects

Nineteen-channel EEGs were recorded from the scalp surface of 30 healthy subjects (16 males and 14 females, mean age: 34 years, SD: 11.7 years) at rest and under trains of intermittent photic stimulation (IPS) at rates of 5, 10 and 20 Hz. Digitalized data were submitted to spectral analysis with fast fourier transformation providing the basis for the computation of global field power (GFP). For quantification, GFP values in the frequency ranges of 5, 10 and 20 Hz at rest were divided by the corresponding data obtained under IPS. All subjects showed a photic driving effect at each rate of stimulation. GFP data were normally distributed, whereas ratios from photic driving effect data showed no uniform behavior due to high interindividual variability. Suppression of alpha-power after IPS with 10 Hz was observed in about 70% of the volunteers. In contrast, ratios of alpha-power were unequivocal in all subjects: IPS at 20 Hz always led to a suppression of alpha-power. Dividing alpha-GFP with 20-Hz IPS by alpha-GFP at rest (R = a-GFPIPS/a-GFPrest) thus resulted in ratios lower than 1. We conclude that ratios from GFP data with 20-Hz IPS may provide a suitable paradigm for further investigations. Key words: EEG, Brain mapping, Intermittent photic stimulation, IPS, Global field power ratio

Doping stigmata as pathological clinical signs in the diagnostic field of sports anthropology

An anthropometric and therefore cost-neutral screening approach as an indicator of the abuse of anabolic steroids by bodybuilders is the fat-free mass index (FFMI). Normalized to a body size of 1.80 m, the FFMI is calculated as follows: FFMI = lean mass (in kg) / body height (in m)² + 6.1 × (1.8 – body height (in m)). Furthermore, various physical and anthropological symptoms can be summarized as evidence of anabolic steroids or growth hormone abuse as doping signs or doping stigmata. Doping stigmata are usually identifiable doping signs in the external appearance. Typical doping signs for anabolic steroids are testicular atrophy, swollen, voluminous muscles with elusive smooth contours, exophthalmus, alopecia androgenetica, steroid acne, gynecomastia, cutis verticis gyrata, striae distensae, seborrhea, hematomas, an unproportional development of the upper body compared to the rest of the body and, in females, hirsutism, hypertrichosis, mammary atrophy and masculine growth in width, extreme reduction of subcutaneous fat percentage, lowering of the voice, clitoris hypertrophy, secondary amenorrhoea and the irreversible androgenization of a female fetus during pregnancy. Doping stigmata for growth hormone are gigantism, acromegaly, macroglossia, tooth gaps, prognathism, torus supraorbitalis, visceromegaly (cardiomyopathy, splenomegaly and hepatomegaly), hairs of wire brush consistency, edema, seborrhoea, skin thickening and hypertrichosis, cutis verticis gyrata and the reduction of the percentage of fat. Typical examples of doping signs of erythropoietin are plethora and rubeosis faciei. Doping stigmata of amphetamines are, for example, tachycardia, hypertension, decongestion, tremor, mydriasis or speed pimples

Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of a pseudo gender classifier and a pseudo face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images