No abstract

Our findings suggest that the effect of the 450 MHz microwave radiation modulated at 7, 14 and 21 Hz varies depending on the modulation frequency. The microwave exposure modulated at 14 and 21 Hz enhanced the EEG power in the alpha and beta frequency bands, whereas no enhancement occurred during exposure to the modulation frequency of 7 Hz.

This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi's fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control subjects were used as a database for calculations. SASI values were positive for depressive and negative for control group (P < 0.05). SASI provided the true detection rate of 88% in the depressive and 82% in the control group. The calculated HFD values detected a small (3%) increase with depression (P < 0.05). HFD provided the true detection rate of 94% in the depressive group and 76% in the control group. The rate of correct indication in the both groups was 85% using SASI or HFD. Statistically significant variations were not revealed between hemispheres (P > 0.05). The results indicated that the linear EEG analysis method SASI and the nonlinear HFD method both demonstrated a good sensitivity for detection of characteristic features of depression in a single-channel EEG.

This study is aimed to compare sensitivity of different electroencephalographic (EEG) indicators for detection of depression. The novel EEG spectral asymmetry index (SASI) was introduced based on balance between the powers of two special EEG frequency bands selected lower and higher of the EEG spectrum maximum and excluding the central frequency from the calculations. The efficiency of the SASI was compared to the traditional EEG inter-hemispheric asymmetry and coherence methods. EEG recordings were carried out on groups of depressive and healthy subjects of 18 female volunteers each. The resting eight-channel EEG was recorded during 30 min. The SASI calculated in an arbitrary EEG channel differentiated clearly between the depressive and healthy group (p < 0.005). Correlation between SASI and Hamilton Depression Rating Scale score was 0.7. The EEG inter-hemispheric asymmetry and coherence revealed some trends, but no significant differences between the groups of healthy controls and patients with depressive disorder.

People express emotions through different modalities. Utilization of both verbal and nonverbal communication channels allows to create a system in which the emotional state is expressed more clearly and therefore easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. This article presents analysis of audiovisual information to recognize human emotions. A cross-corpus evaluation is done using three different databases as the training set (SAVEE, eNTERFACE'05 and RML) and AFEW (database simulating realworld conditions) as a testing set. Emotional speech is represented by commonly known audio and spectral features as well as MFCC coefficients. The SVM algorithm has been used for classification. In case of facial expression, faces in key frames are found using Viola-Jones face recognition algorithm and facial image emotion classification done by CNN (AlexNet). Multimodal emotion recognition is based on decision-level fusion. The performance of emotion recognition algorithm is compared with the validation of human decision makers.