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

Stress and Emotion are complex phenomena that play significant roles in the quality of human life. Emotion plays a major role in motivation, perception, cognition, creativity, attention, learning and decision-making (Seymour et al., 2008). A major problem in understanding emotion is the assessment of the definition of emotions. In fact, even psychologists have problem agreeing on what is considered an emotion and how many types of emotions exist. Kleinginna gathered and analyzed 92 definitions of emotion from literature present that day. He concludes that Emotion is a complex set of interactions among subjective and objective factors, mediated by neural/hormonal systems (Horlings, 2008). In fact, Emotion is a subcategory of stress. A lot of research has been undertaken in assessment of stress and emotion over the last years. Most of researches in the domain of stress and emotional states use peripheral signals such as respiratory rate, Skin Conductance (SC), Blood Volume Pulse (BVP) (Zhai et al., 2006) and Temperature (McFarland, 1985). Most previous research, have investigated the use of EEG and peripheral signals separately, but little attention has been paid so far to the fusion between EEG and peripheral signals (Chanel, 2009; Chanel et al., 2009; Hosseini, 2009). In one study, Aftanas et al. (2004) that showed significant differentiation of arousal based on EEG data collected from participants watching high, intermediate and low arousal images. Chanel (2009) asked the participants to remember past emotional episodes, and obtained the accuracy of 88% using EEG for 3 categories with Support Vector Machine (SVM) classifier. Hosseini et al. (2009) used the induction visual images based acquisition protocol for recording the EEG and peripheral signals under 2 categories of emotional stress states (Calm-neutral and Negatively-exited) of participants, and obtained the accuracy of 78.3% using EEG signals with SVM classifier. Kim et al. (2004) used the combination of music and story as stimuli and there were 50 participants, to introduce a user independent system, the results showed the accuracy of 78.4% and 61% for 3 and 4 categories of different emotions respectively. Takahashi (2004) used film clips to stimulate participants with five different emotions, resulting in 42% of correctly identified patterns. Schaaff & Schultz (2009) used pictures from the International Affective Picture System (IAPS) to induce three emotional states: pleasant, neutral, and unpleasant. They obtained the accuracy of 66.7% for three classes of emotion, solely based on EEG signals.

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Classification of Emotional Stress Using Brain Activity

Stress can be perceived as the reaction of the body towards observed mental, emotional and physical distress. It is highly curable if it is detected and managed early. There are various methods in relieving anxiety and stress such as meditation. However, to some people, this act is hard to perform. Therefore, they switch to another therapy which is easier and simpler but deliver similar side effects as meditation i.e. brainwave entrainment. Binaural beat is one type of such brainwave entrainments, in which two signals with different frequency are presented to each ear simultaneously. In regards to this, our research work focuses on studying the effects of binaural-beats to people with stress, specifically the effects to their EEG signals. In such context, this paper presents the results collected from the experiments performed on a number of participants. EEG signals of the participants are recorded in three different states namely the initial state, after stress induction and after binaural-beats entrainment. The Alpha Symmetry method has been employed in analyzing the results especially to study the effect of stress induced and binaural beats entrainment on the participants' alpha waves.

Behaviour of EEG Alpha Asymmetry when stress is induced and binaural beat is applied

Objective A passive brain-computer interface (pBCI) is a system that continuously adapts human-computer interaction to the user's state. Key to the efficacy of such a system is the reliable estimation of the user's state via neural signals, acquired through non-invasive methods like electroencephalography (EEG) or near-infrared spectroscopy (fNIRS). Many studies to date have explored the detection of mental workload in particular, usually for the purpose of improving safety in high risk work environments. In these studies, mental workload is generally modulated through the manipulation of task difficulty, and no other aspect of the user's state is taken into account. In real-life scenarios, however, different aspects of the user's state are likely to be changing simultaneously-for example, their cognitive state (e.g. level of mental workload) and affective state (e.g. level of stress/anxiety). This inevitable confounding of different states needs to be accounted for in the development of state detection algorithms in order for them to remain effective when taken outside the lab. Approach In this study we focussed on two different states that are of particular importance in high risk work environments, specifically mental workload and stress, and explored the effect of each on the ability to detect the other using EEG signals. We developed an experimental protocol in which participants performed a cognitive task under two different levels of workload (low workload and high workload) and at two levels of stress (relaxed and stressed) and then used a linear discriminant classifier to perform classification of workload level and stress level independently. Main results We found that the detection of both mental workload level (e.g. low workload vs. high workload) and stress level (e.g. stressed vs. relaxed) were significantly diminished if the training and test data came from different as opposed to the same level of the other state (e.g. for mental workload classification, training on data from a relaxed condition and testing on data from a stressed condition, rather than both training and testing on the relaxed condition). The reduction in classification accuracy observed was as much as 15%. Significance The results of this study indicate the importance of considering multiple aspects of a user's state when developing detection algorithms for pBCI technologies.

EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other

Development of affective Brain-Computer Interfaces (BCIs) via Electroencephalogram (EEG) has emerged as a cynosure of research in early diagnosis and effective management of depression. However, conventional BCIs are still lacking in terms of high computational complexity, less accuracy due to Fourier phase suppression and lack of substantial conclusion for depression diagnosis. An automated, EEG-based depression diagnostic and management tool is proposed to overcome these limitations. Channel event-related potentials, cross-coherence and power spectra plots in MATLAB are quantified and studied as an outcome to map real-time, emotion-specific multichannel EEG data set into distinct emotional states. A fast and stable fourth-order statistics-based independent component analysis is incorporated to reject temporal/spatial artefacts. Increases in frontal alpha (8-13 Hz) and delta (0.5-4 Hz) power/coherence are during depressed and normal/relaxed states, respectively. Devotional music (relaxed state) is found to facilitate depression elimination. Results are found to be statistically significant across all subjects with minimal p-values. Hence, it has been inferred that the proposed model has the potential to aid early and accurate depression diagnostic and management process.

Depression diagnosis and management using EEG-based affective brain mapping in real time

Introduction: This paper proposes a new emotional stress detection system using multi-modal bio-signals. Since EEG is the reflection of brain activity and is widely used in clinical diagnosis and biomedical researches, it is used as the main signal. Methods and Materials: We designed an efficient IAPS acquisition protocol to acquire the EEG and psychophysiological signals under picture induction environment (calm-neutral and negatively excited) for participants. Data such as skin conductance (SC), Blood Volume Pulse (BVP), respiratory rate (RR) and EEG were continuously recorded through bio-sensors placed on the participant. In order to choose the proper EEG channels we used the cognitive model of the brain under emotional stress. Results: After pre-processing the bio-signals, linear features were employed to extract the psychophysiological signals and chaotic (or nonlinear) invariants like; fractal dimension by Higuchi’s algorithm, correlation dimension and approximate entropy were used to extract the characteristics of the EEG signals. For emotional stress detection, Genetic Algorithm (GA) and Elman neural network are applied to design the emotional stress classifier are investigated. The results show that, classification accuracy with fusion link between EEG and psychophysiological signals was 82.6% using the Elman classifier in two classes from emotional stress

V Niazmand

Papers

Qualitative and quantitative evaluation of brain activity in emotional stress