Classification Of Emotional States Research Articles

Fundamental Frequency Extraction Method using Central Clipping and its Importance for the Classification of Emotional State

The paper deals with a classification of emotional state. We implemented a method for extracting the fundamental speech signal frequency by means of a central clipping and examined a correlation between emotional state and fundamental speech frequency. For this purpose, we applied an approach of exploratory data analysis. The ANOVA (Analysis of variance) test confirmed that a modification in the speaker's emotional state changes the fundamental frequency of human vocal tract. The main contribution of the paper lies in investigation, of central clipping method by the ANOVA.

Harmony Search 알고리즘 기반 HMM 구조 최적화에 의한 얼굴 정서 인식 시스템 개발

본 논문에서는 얼굴 표정에서 나타나는 동적인 정서상태 변화를 고려한 얼굴 영상 기반 정서 인식 연구를 제안한다. 본 연구는 얼굴 영상 기반 정서적 특징 검출 및 분석 단계와 정서 상태 분류/인식 단계로 구분할 수 있다. 세부 연구의 구성 중 첫 번째는 Facial Action Units (FAUs)과 결합한 Active Shape Model (ASM)을 이용하여 정서 특징 영역 검출 및 분석기법의 제안이며, 두 번째는 시간에 따른 정서 상태의 동적 변화를 고려한 정확한 인식을 위하여 Hidden Markov Model(HMM) 형태의 Dynamic Bayesian Network를 사용한 정서 상태 분류 및 인식기법의 제안이다. 또한, 최적의 정서적 상태 분류를 위한 HMM의 파라미터 학습 시 Harmony Search (HS) 알고리즘을 이용한 휴리스틱 최적화 과정을 적용하였으며, 이를 통하여 동적 얼굴 영상 변화를 기반으로 하는 정서 상태 인식 시스템을 구성하고 그 성능의 향상을 도모하였다. In this paper, we propose an study of the facial emotion recognition considering the dynamical variation of emotional state in facial image sequences. The proposed system consists of two main step: facial image based emotional feature extraction and emotional state classification/recognition. At first, we propose a method for extracting and analyzing the emotional feature region using a combination of Active Shape Model (ASM) and Facial Action Units (FAUs). And then, it is proposed that emotional state classification and recognition method based on Hidden Markov Model (HMM) type of dynamic Bayesian network. Also, we adopt a Harmony Search (HS) algorithm based heuristic optimization procedure in a parameter learning of HMM in order to classify the emotional state more accurately. By using all these methods, we construct the emotion recognition system based on variations of the dynamic facial image sequence and make an attempt at improvement of the recognition performance.

Real-time support vector classification and feedback of multiple emotional brain states

An important question that confronts current research in affective neuroscience as well as in the treatment of emotional disorders is whether it is possible to determine the emotional state of a person based on the measurement of brain activity alone. Here, we first show that an online support vector machine (SVM) can be built to recognize two discrete emotional states, such as happiness and disgust from fMRI signals, in healthy individuals instructed to recall emotionally salient episodes from their lives. We report the first application of real-time head motion correction, spatial smoothing and feature selection based on a new method called Effect mapping. The classifier also showed robust prediction rates in decoding three discrete emotional states (happiness, disgust and sadness) in an extended group of participants. Subjective reports ascertained that participants performed emotion imagery and that the online classifier decoded emotions and not arbitrary states of the brain. Offline whole brain classification as well as region-of-interest classification in 24 brain areas previously implicated in emotion processing revealed that the frontal cortex was critically involved in emotion induction by imagery. We also demonstrate an fMRI-BCI based on real-time classification of BOLD signals from multiple brain regions, for each repetition time (TR) of scanning, providing visual feedback of emotional states to the participant for potential applications in the clinical treatment of dysfunctional affect.

Classification of human emotion from EEG using discrete wavelet transform

In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used “db4” wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.

Bodily Feeling in Emotion

One might imagine that this remark of James was too obvious to be denied, but in fact current philosophical orthodoxy runs against it. Since the renewal of interest in the emotions produced by Anthony Kenny's Action Emotion and Will in 1963, philosophers have focussed primarily on the cognitive aspects of emotions—the judgments, evaluations, beliefs, presuppositions which they contain. Bodily feelings have been, on the whole, slighted. Sometimes they are dismissed outright, as by Robert Solomon: “feelings no more constitute or define the emotion than an army of fleas constitutes a homeless dog. … Feeling is the ornamentation of emotion, not its essence.”; more often, as in Jerome Neu, given only rather slight attention because “thoughts are of greater importance than feelings (in the narrow sense of felt sensations) in the classification and discrimination of emotional states”.

Emotion, Thought and Therapy.

 Hume and Spinoza are the most systematic representatives of two opposing traditions of argument about the relation of thought and feeling in the emotions. The Humeans treat emotions as essentially feelings (impressions or affects) with thoughts incidentally attached. The Spinozists say roughly the reverse, treating emotions as essentially thoughts ('ideas' or 'beliefs') with feelings incidentally attached. It is argued that the Spinozists are closer to the truth, that is, that thoughts are of greater importance than feelings fin the narrow sense of felt sensations) in the classification and discrimination of emotional states. It is then argued that if the Spinozists are closer to the truth, we have the beginning of an argument to show that Freudian or, more generally, analytic therapies make philosophic sense. That is, we can begin to understand how people's emotional lives might be trans- formed by consideration and interpretation of their memories, beliefs, etc.; how knowledge might help make one free. [continued in text ...]