Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.

/pdf/deap-a-database-for-emotion-analysis-using-physiological-1b41jp6d2i.pdf

DEAP: A Database for Emotion Analysis ;Using Physiological Signals

http://gruberpeplab.com/3131/Kreibig_2010.pdf

Autonomic nervous system activity in emotion: A review

One of the fastest growing areas within psychology is the field of emotion regulation. However, enthusiasm for this topic continues to outstrip conceptual clarity, and there remains considerable uncertainty as to what is even meant by “emotion regulation.” The goal of this review is to examine the current status and future prospects of this rapidly growing field. In the first section, I define emotion and emotion regulation and distinguish both from related constructs. In the second section, I use the process model of emotion regulation to selectively review evidence that different regulation strategies have different consequences. In the third section, I introduce the extended process model of emotion regulation; this model considers emotion regulation to be one type of valuation, and distinguishes three emotion regulation stages (identification, selection, implementation). In the final section, I consider five key growth points for the field of emotion regulation.

Emotion Regulation: Current Status and Future Prospects

Researchers have wondered how the brain creates emotions since the early days of psychological science. With a surge of studies in affective neuroscience in recent decades, scientists are poised to answer this question. In this target article, we present a meta-analytic summary of the neuroimaging literature on human emotion. We compare the locationist approach (i.e., the hypothesis that discrete emotion categories consistently and specifically correspond to distinct brain regions) with the psychological constructionist approach (i.e., the hypothesis that discrete emotion categories are constructed of more general brain networks not specific to those categories) to better understand the brain basis of emotion. We review both locationist and psychological constructionist hypotheses of brain-emotion correspondence and report meta-analytic findings bearing on these hypotheses. Overall, we found little evidence that discrete emotion categories can be consistently and specifically localized to distinct brain regions. Instead, we found evidence that is consistent with a psychological constructionist approach to the mind: A set of interacting brain regions commonly involved in basic psychological operations of both an emotional and non-emotional nature are active during emotion experience and perception across a range of discrete emotion categories.

/pdf/the-brain-basis-of-emotion-a-meta-analytic-review-32zzrbb78z.pdf

The brain basis of emotion: A meta-analytic review

Responses to fear- and sadness-inducing films were assessed using a broad range of cardiovascular (heart rate, T-wave amplitude, low- and high-frequency heart rate variability, stroke volume, preejection period, left-ventricular ejection time, Heather index, blood pressure, pulse amplitude and transit time, and finger temperature), electrodermal (level, response rate, and response amplitude), and respiratory (rate, tidal volume and its variability, inspiratory flow rate, duty cycle, and end-tidal pCO(2)) measures. Subjective emotional experience and facial behavior (Corrugator Supercilii and Zygomaticus Major EMG) served as control measures. Results indicated robust differential physiological response patterns for fear, sadness, and neutral (mean classification accuracy 85%). Findings are discussed in terms of the fight-flight and conservation-withdrawal responses and possible limitations of a valence-arousal categorization of emotion in affective space.

Cardiovascular, electrodermal, and respiratory response patterns to fear- and sadness-inducing films.

The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k-nearest neighbors method), and cross-validation method (subject- and stimulus-(in)dependence). Analyses were run on a data set of 34 participants watching two sets of three 10-min film clips (fearful, sad, neutral) while autonomic, respiratory, and facial muscle activity were assessed. Results demonstrate that the three states can be classified with high accuracy by most classifiers, with the sparsest model having only five features, even for the most difficult task of identifying the emotion of an unknown subject in an unknown situation (77.5%). Implications for choosing PCA parameters are discussed.

An affective computing approach to physiological emotion specificity: toward subject-independent and stimulus-independent classification of film-induced emotions.

We describe the creation of a film library designed for researchers interested in positive (amusing), negative (repulsive), mixed (amusing and repulsive) and neutral emotional states. Three hundred 20- to 33-second film clips videotaped by amateurs were selected from video-hosting websites and screened in laboratory studies by 75 female participants on self-reported amusement and repulsion (Experiments 1 and 2). On the basis of pre-defined cut-off values, 51 positive, 39 negative, 59 mixed and 50 neutral film clips were selected. These film clips were then presented to 411 male and female participants in a large online study to identify film clips that reliably induced the target emotions (Experiment 3). Depending on the goal of the study, researchers may choose positive, negative, mixed or neutral emotional film clips on the basis of Experiments 1 and 2 or Experiment 3 ratings.

/pdf/eliciting-positive-negative-and-mixed-emotional-states-a-1sue7venq3.pdf

Eliciting positive, negative and mixed emotional states: A film library for affective scientists.

How to conceptualize mixed emotional states is a central issue in the field of affective science. Nondifferentiation, additive, and emergence accounts of mixed emotions make divergent predictions regarding physiological responses in mixed emotions. To test these predictions, 43 women watched film clips that elicited amusement, disgust, or mixed emotions while feeling self-report, facial electromyography, cardiovascular, electrodermal, and respiratory measures were assessed. Simultaneous self-reports of amusement and disgust confirmed elicitation of a mixed emotional state. Physiologically, mixed emotions differed from pure amusement and pure disgust both in intensity and pattern. This suggests a distinct physiological response of the mixed emotional state, as predicted by the emergence account of mixed emotions. Implications for emotion theory and research are discussed.

/pdf/the-psychophysiology-of-mixed-emotional-states-2bimw7ykyc.pdf

Sylvia D. Kreibig

Papers

Autonomic nervous system activity in emotion: A review

Cardiovascular, electrodermal, and respiratory response patterns to fear- and sadness-inducing films.

An affective computing approach to physiological emotion specificity: toward subject-independent and stimulus-independent classification of film-induced emotions.

Eliciting positive, negative and mixed emotional states: A film library for affective scientists.

The psychophysiology of mixed emotional states