Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

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.

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DEAP: A Database for Emotion Analysis ;Using Physiological Signals

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral signals of the players playing a Tetris game at three difficulty levels. This analysis confirms that the different difficulty levels correspond to distinguishable emotions, and that, playing several times at the same difficulty level gives rise to boredom. The next step was to train several classifiers to automatically detect the three emotional classes from EEG and peripheral signals in a player-independent framework. By using either type of signals, the emotional classes were successfully recovered, with EEG having a better accuracy than peripheral signals on short periods of time. After the fusion of the two signal categories, the accuracy raised up to 63%.

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Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

The arousal dimension of human emotions is assessed from two different physiological sources: peripheral signals and electroencephalographic (EEG) signals from the brain. A complete acquisition protocol is presented to build a physiological emotional database for real participants. Arousal assessment is then formulated as a classification problem, with classes corresponding to 2 or 3 degrees of arousal. The performance of 2 classifiers has been evaluated, on peripheral signals, on EEG's, and on both. Results confirm the possibility of using EEG's to assess the arousal component of emotion, and the interest of multimodal fusion between EEG's and peripheral physiological signals.

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Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals

The work presented in this paper aims at assessing human emotions using peripheral as well as electroencephalographic (EEG) physiological signals on short-time periods Three specific areas of the valence-arousal emotional space are defined, corresponding to negatively excited, positively excited, and calm-neutral states An acquisition protocol based on the recall of past emotional life episodes has been designed to acquire data from both peripheral and EEG signals Pattern classification is used to distinguish between the three areas of the valence-arousal space The performance of several classifiers has been evaluated on 10 participants and different feature sets: peripheral features, EEG time-frequency features, EEG pairwise mutual information (MI) features Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEGs to assess valence and arousal in emotion recall conditions The obtained accuracy for the three emotional classes is 63% using EEG time-frequency features, which is better than the results obtained from previous studies using EEG and similar classes Fusion of the different feature sets at the decision level using a summation rule also showed to improve accuracy to 70% Furthermore, the rejection of non-confident samples finally led to a classification accuracy of 80% for the three classes

/pdf/short-term-emotion-assessment-in-a-recall-paradigm-1zh6el5f7p.pdf

Short-term emotion assessment in a recall paradigm

This article reviews the psychophysiological method in game research. Psychophysiological measurements provide an objective, continuous, real-time, noninvasive, precise and sensitive way to assess the game experience. However, the best results require controlled experiments with careful monitoring of variables, large enough sample sizes and expertise in electrical signal processing. We briefly explain the theory behind the method and present the most useful measures: electromyography (EMG), electrodermal activity (EDA), electroencephalography (EEG) and cardiac measures. We review previous studies that have used psychophysiological measures in game research and illustrate some future directions. Our article covers several research lines using the psychophysiological method in game studies, and offers a comprehensive list of references for those interested in the field.

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A review of the use of psychophysiological methods in game research

Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity.

Guillaume Chanel

Papers

Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals

Short-term emotion assessment in a recall paradigm

A review of the use of psychophysiological methods in game research

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges