This article reports the results of the study related to emotion recognition by using eye-tracking. Emotions were evoked by presenting a dynamic movie material in the form of 21 video fragments. Eye-tracking signals recorded from 30 participants were used to calculate 18 features associated with eye movements (fixations and saccades) and pupil diameter. To ensure that the features were related to emotions, we investigated the influence of luminance and the dynamics of the presented movies. Three classes of emotions were considered: high arousal and low valence, low arousal and moderate valence, and high arousal and high valence. A maximum of 80% classification accuracy was obtained using the support vector machine (SVM) classifier and leave-one-subject-out validation method.

Although the psychophysiological signs of fatigue are well known, automatic methods for the detection of fatigue in employees in specific working conditions are still lacking. Many people do repetitive work on computers and become fatigued; therefore, the detection of fatigue in employees can help prevent accidents and increase their work efficiency. In this paper, we propose an algorithm for the effective detection of fatigue which is based only on electrooculographic (EOG) signal. Three features were assessed: blink duration, blink amplitude, and time between blinks. To cause fatigue, the N-back test, lasting for 60 minutes, was carried out. The article presents the research results for 24 users. The effectiveness of the proposed system was measured by the accuracy of classification. The average classification accuracy was 0.93 for user-dependent mode and 0.89 for user-independent mode. The results of the conducted experiments indicated that assessing the three proposed features can help in the effective detection of fatigue in users.

The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS patterns. One hundred twenty eight channel EEG signal was used in the experiments. The signal was recorded for 40 people, during the process of imagining right and left hand movements. Feature extraction was performed using frequency analysis (FFT) with the resolution of 1Hz. So the features were spectral lines associated with particular electrodes. The selection of features, calculated for all people, was made with GA. The fitness function used in GA was EEG signal classification error calculated using LDA classifier and 5-CV test. The average accuracy of the classification for all people in 8-30Hz band was 0.85, while for the top 10 results 0.92.

Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informuj?ce swoich czytelnik?w o najnowszych osi?gni?ciach naukowych i nowoczesnych rozwi?zaniach technicznych w Polsce i na ?wiecie, popularyzuje problemy techniczne oraz poszerza wiedz? i kultur? techniczn?.