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A novel steady state visual evoked potential (SSVEP)-based BCI system for driver's sleepiness monitoring is proposed. Detecting the driver's concentration is one of the most challenging assignments for researchers in these decades. A continuous attention and awareness is necessary to drive safely. Otherwise, one tragedy of drowsy driving would probably happen. Therefore, real-time sleepiness detection can restrain accidents effectively. In this study, SSVEPs are used for running the proposed system and two experimental setups consisting four single and paired lightemitting diodes (LEDs) using two different fast Fourier transform-based feature extraction methods, and three different classifiers of the linear discriminant analysis, the support vector machine (SVM) and the Max ones on the accuracy of the system are studied. For real-time application, related features are extracted from four different sweep lengths (temporal durations) of 0.5, 1, 2, and 3 s. The experimental results show that higher sweeps have higher accuracies and the SVM classifier, experimental setup of 4-paired LEDs in sweep length of 3 s has the highest accuracy of 98.2 %, while with the comparable information transfer rate (ITR) value of 24 bits/min within the sweep length of 1 s, this time is considered as the best response time. Therefore, this study demonstrates the feasibility of the proposed system in a practical driving application.

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Dynamic variations of electroencephalogram (EEG) contain significant information in the study of human emotional states. Transient time methods are well suited to evaluate short-term dynamic changes in brain activity. Human affective states, however, can be more appropriately analyzed using chaotic dynamical techniques, in which temporal variations are considered over longer durations. In this study, we have applied two different recurrence-based chaotic schemes, namely the Poincaré map function and recurrence plots (RPs), to analyze the long-term dynamics of EEG signals associated with state space (SS) trajectory of the time series. Both approaches determine the system dynamics based on the Poincaré recurrence theorem as well as the trajectory divergence producing two-dimensional (2D) characteristic plots. The performance of the methods is compared with regard to their ability to distinguish between levels of valence, arousal, dominance and liking using EEG data from the “dataset for emotion analysis using physiological” database. The differences between the levels of emotional feelings were investigated based on the analysis of variance (ANOVA) test and Spearman’s statistics. The results obtained from the RP features distinguish between the emotional ratings with a higher level of statistical significance as compared with those produced by the Poincaré map function. The scheme based on RPs was particularly advantageous in identifying the levels of dominance. Out of the 32 EEG electrodes examined, the RP-based approach distinguished the dominance levels in 23 electrodes, while the approach based on the Poincaré map function was only able to discriminate dominance levels in five electrodes. Furthermore, based on nonlinear analysis, significant correlations were observed over a wider area of the cortex for all affective states as compared with that reported based on the analysis of EEG power bands.