There are three main views in computational neuroscience including deterministic, stochastic, and Nonlinear approaches. In the deterministic ap-proach, the human brain is considered a linear and stationary system with determined parameters. . . .

Background: Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior. Methods: In this study, an attempt is made to extract important Nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors. Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach. Conclusion: Our findings approve of the effectiveness of using Nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation.

Introduction: In this paper, Nonlinear dynamical Analysis based on Recurrence Quantification Analysis (RQA) is employed to characterize the Nonlinear EEG dynamics. RQA can provide useful quantitative information on the regular, chaotic, or stochastic property of the underlying dynamics.Methods: We use the RQA-based measures as the quantitative features of the Nonlinear EEG dynamics. Mutual Information (MI) was used to find the most relevant feature subset out of RQA-based features. The selected features were fed into an artificial neural network for grouping of EEG recordings to detect ictal, interictal, and healthy states. The performance of the proposed procedure was evaluated using a database for different classification cases.Results: The combination of five selected features based on MI achieved 100% accuracy, which demonstrates the superiority of the proposed method.Conclusion: The results showed that the Nonlinear dynamical Analysis based on Rcurrence Quantification Analysis (RQA) can be employed as a suitable approach for characterizing the Nonlinear EEG dynamics and detecting the seizure

In this study, a neuromarketing project was conducted via EEG signal processing in which the individuals’ interest for buying a relatively luxurious decorative product (which has a relative advantage in exports based on commonly evaluated criteria and indicators in economic) was evaluated. EEG signals of 24 participants during observing and selecting gemstone images were recorded and processed in order to analyze statistical significance of brain activity variations involved in the emotional (liking) and the decision making (choosing) processes. The recorded signals during the stimulation and selection phases were pre-processed in several steps to remove the existing noises and artifacts. Then, the 19-channel EEGs were processed via multiple tools to indicate active brain regions while watching gemstones. Brain mapping and regional Analysis indicated that the occipital>frontal>limbic regions were more activated than other regions. Moreover, the left hemisphere has been more active than the right hemisphere. At the next step, Nonlinear entropy feature of each signal segment was extracted to be used for training a neurofuzzy system which is an automatic classifier that learns to classify the individuals’ choices. The classification has resulted in 86. 25% precision and 87. 4% accuracy in a three-class classification task (including two pleasant selections and one unpleasant selection). At the final step, using a questionnaire filled by participants following the recording session, a number of statistical analyses were performed over the self-conscious and unconscious by means of statistical tools including t-test, Analysis of variance and regression. The results of statistical tests indicated that there are significant differences for the cognition of liking or preferring among different choices and based on the selections made by women and men. Furthermore, the lack of existence of a significant difference between conscious and unconscious choices were rejected.

Background: Epilepsy is a common neurological disorder with a prevalence of 1% of the world population. Absence epilepsy is a form of generalized seizures with Spike wave discharge in EEG. Epileptic patients have frequent absence seizures that cause immediate loss of consciousness. Methods: In this study, it has been tried to explore whether EEG changes can effectively detect epilepsy in animal model applying non-linear features. To predict the occurrence of absence epilepsy, a long-term EEG signal has been recorded from frontal cortex in seven Wag/Rij rats. After preprocessing, the data was transferred to the phase space to extract the brain system dynamic and geometric properties of this space. Finally, the ability of each features to predict and detect absence epilepsy with two criteria of predictive time and the accuracy of detection and its results were compared with previous studies. Results: The results indicate that the brain system dynamic changes during the transition from free-seizure to pre-seizure and then seizure. Proposed approach diagnostic characteristics yielded 97% accuracy of absence epilepsy diagnosis indicating that due to the Nonlinear and complex nature of the system and the brain signal, the use of methods consistent with this nature is important in understanding the dynamic transfer between different epileptic seizures. Conclusion: By changing the state of the absence Seizures, the dynamics are changing, and the results of this research can be useful in real-time applications such as predicting epileptic seizures.

Aims and background: This study develops a computational framework for the classification of different anesthesia states, including awake, moderate anesthesia, and general anesthesia, using electroencephalography (EEG) signals and peripheral parameters.Materials and Methods: The proposed method proposes data gathering; preprocessing; a new labeling process of EEG signal; appropriate selection of window length by genetic algorithm; feature extraction by Hjorth parameters, approximate entropy, Petrosian fractal dimension, Hurst exponent, largest Lyapunov exponent, Lempel-Ziv complexity, correlation dimension, and Daubechies wavelet coefficients; feature normalization; feature selection by non-negative sparse principal component Analysis; and classification by radial basis function (RBF) neural network. Correct labeling process of EEG signals is performed by an expert opinion and also qualitative and quantitative Analysis of the extracted parameters from peripheral nerve stimulator, pulse oximetry, blood pressure, and the time of drug injection.Findings: The results indicate that the proposed method would classify different anesthesia states including awake, moderate anesthesia, and general anesthesia, with the accuracy of 93.98%, 98.62, and 97.3, respectively. Therefore, the proposed method can classify different anesthesia states with the average accuracy of 97.3%.Conclusion: Finally, the proposed method provided a good representation of the brain behavior in different anesthesia states.

Plastic hinge properties play a crucial role in predicting the Nonlinear response of structural elements. The plastic hinge region of reinforced concrete normal beams has been previously studied experimentally and analytically. The main objective of this research is to evaluate the behavior of the plastic hinge region of reinforced concrete deep beams and its comparison with normal beams through finite element simulation. To do so, ten beams contain six deep beams, and four normal beams, under concentrated and uniformly distributed loading, are investigated. Lengths in the plastic hinge region involving curvature localization, rebar yielding, and concrete crushing zones are studied. The results indicate that the curvature localization zone is not suitable for the prediction of plastic hinge length in reinforced concrete deep beams. Based on the results it can be stated that in simply supported normal beams the concrete crushing zone is focused on the middle span, but in simply supported deep beams by creating a compression strut between loading place and support, the concrete crushing zone spreads along the compression trajectory. The rebar yielding zone of simply supported beams increases as the loading type is changed from the concentrated load at the middle to the uniformly distributed load.