I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

Feature Extraction and Selection for Emotion Recognition from EEG

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

Emotions Recognition Using EEG Signals: A Survey

https://bcmi.sjtu.edu.cn/~blu/papers/2014/2.PDF

Emotional state classification from EEG data using machine learning approach

Objective measures, sensors and computational techniques for stress recognition and classification

this paper proposes a new emotional stress recognition system using multi-modal bio-signals. Since electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research, it is used as the main signal. In order to choose the proper EEG channels we used the cognitive model of the brain under emotional stress. We designed an efficient acquisition protocol to acquire the EEG and psychophysiological signals under pictures induction environment (calm-neutral and negative-excited) for participants. Qualitative and quantitative evaluation of psychophysiological signals have been tried to select suitable segments of EEG signal for improving efficiency and performance of emotional stress recognition system. After pre-processing the signals, both Linear and nonlinear features were employed to extract the EEG parameters. Wavelet coefficients and chaotic invariants like fractal dimension by Higuchi's algorithm and correlation dimension were used to extract the characteristics of the EEG signal which showed that the classification accuracy in two emotional states was 82.7% using the Elman classifier. This is a great improvement in results compared with other similar published work.

Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State

This paper proposes an emotional stress recognition system with EEG signals using higher order spectra (HOS). A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional stress states of participants, Calm neutral and Negatively exited. After pre-processing the signals, higher order spectra are employed to extract the features for classifying human emotions. We used Genetic Algorithm for optimum features selection for the classifier. Using the SVM classifier, our study achieved an average accuracy of 82% for the two-abovementioned emotional stress states. We concluded that HOS analysis could be an accurate tool in the assessment of human emotional stress states. We achieved to same results compared to our previous studies.

/pdf/higher-order-spectra-analysis-of-eeg-signals-in-emotional-18befotkkq.pdf

Higher Order Spectra Analysis of EEG Signals in Emotional Stress States

This paper proposes an emotion recognition system using EEG signals, therefore a new approach to emotion state analysis by approximate (ApEn) and wavelet entropy (WE) is described. We have used EEG signals recorded during emotion in five channels (FP1, FP2, T3, T4 and Pz), under pictures induction environment (calm- neutral and negative excited) for participants. After a brief introduction to the concept, the ApEn and WE were extracted from two different EEG time series. The result showed that, the classification accuracy in two emotion states was 73.25% using the support vector machine (SVM) classifier. The simulations showed that the classification accuracy is good and the proposed methods are effective. During an emotion, the EEG is less complex compared to the normal, indicating reduction in active neuronal process in the brain.

/pdf/emotion-recognition-method-using-entropy-analysis-of-eeg-qp3vz120hx.pdf

Emotion recognition method using entropy analysis of EEG signals

In this paper, we propose a new approach to classify emotional stress in the two main areas of the valance-arousal space by using bio-signals. Since electroencephalogram (EEG) is widely used in biomedical research, it is used as the main signal. We designed an efficient acquisition protocol to acquire the EEG and psychophysiological. Two specific areas of the valencearousal emotional stress space are defined, corresponding to negatively excited and calmneutral states. Qualitative and quantitative evaluation of psychophysiological signals have been used to select suitable segments of EEG signal for improving efficiency and performance of emotional stress recognition system. After pre-processing the EEG signals, wavelet coefficients and chaotic invariants like fractal dimension, correlation dimension and wavelet entropy were used to extract the features of the signal. So, by using independent-sample TTest and Linear Discriminate Analysis (LDA), effective features are selected. The results show that, the average classification accuracy were 80.1% and 84.9% for two categories of emotional stress states using the LDA and Support Vector Machine (SVM) classifiers respectively. We achieved an improvement in accuracy, in compared to our previous studies in the similar field. Therefore, this new fusion link between EEG and psychophysiological signals are more robust in comparison to the separate signals.

Seyyed Abed Hosseini

Papers

Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State

Higher Order Spectra Analysis of EEG Signals in Emotional Stress States

Emotion recognition method using entropy analysis of EEG signals

Emotional stress recognition system for affective computing based on bio-signals

Fischer–Tropsch synthesis over Ru promoted Co/γ-Al2O3 catalysts in a CSTR