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Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

Background: Very little is known about the neural circuitry underlying anger processing among alcoholics. The purpose of this study was to examine the altered brain activity of alcoholic individuals during transient anger emotion. Methods: Using functional magnetic resonance imaging (fMRI), 18 male patients diagnosed with alcohol dependence in an inpatient alcohol treatment facility and 16 social drinkers with similar demographics were scanned during the viewing of anger-provoking film clips. Results: While there was no significant difference in the level of experienced anger between alcohol-dependent patients and non-alcoholic controls, significantly greater activation was observed in the bilateral dorsal anterior cingulate cortex (dACC) and the right precentral gyrus among alcoholic patients compared to the normal controls. Conclusions: In summary, specific brain regions were identified that are associated with anger among patients with alcohol dependency.

In biomedical engineering application, mental/physical health monitoring using biosensors has been lately noticed because bio-signal acquisition by non-invasive sensors is relatively simple as well as bio-signal is less sensitive to social/cultural difference. In particular, although it is known that they are significantly correlated with human emotional state, whether the signals by various emotions are stable remains unknown. In this study, we examined the consistency of physiological responses induced by six basic emotions, happiness, sadness, anger, fear, disgust and surprise using an experiment that was repeated 10 times. Twelve college subjects participated in this experiment. For emotion induction, sixty different emotional stimuli were selected in a pilot experiment. Heart Rate (HR), Skin Conductance Level (SCL), mean of Skin Temperature (meanSKT), and mean of Photoplethysmograph (meanPPG) were measured before the presentation of stimuli as a baseline and during the presentation of the stimuli as emotional state. The results showed that physiological responses during emotional states for the 10 times the experiment was repeated were stable and consistent compared to the baseline. In particular, we could identify that physiological features such as SCL, HR, and PPG are very reliable. Our results suggest that bio-signals by six emotions are consistent over time regardless of various stimuli. This means that physiological responses are reliable and biosensors are useful tool for emotion recognition.

Recently, the most popular research in the field of emotion recognition on human-computer interaction is to recognize human's feeling using various physiological signals. In the psychophysiological research, it is known that there is strong correlation between human emotion state and physiological reaction. In this study, seven kinds of emotion (happiness, sadness, anger, fear, disgust, surprise, stress) are evoked by audio-visual film clips as stimulation, and then autonomic nervous system responses as physiological signals are measured as the reaction of stimulation. In addition that, seven different emotions will be classified by the proposed classification methodology using physiological signals. We introduce a classification methodology on instance-based learning with feature selection that dwells upon the usage of evolutionally inspired optimization technique of Genetic Algorithms (GAs). In classification problems, it becomes important to carefully select prototypes and establish a subset of features in order to achieve a sound performance of a classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness of the approach for the classification of seven emotions. Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner is characterized by high classification accuracy for the seven emotions based on physiological signals.

Objective: The aim of this study is to investigate facial temperature changes induced by facial expression and emotional state in order to recognize a persons emotion using facial thermal images. Background: Facial thermal images have two advantages compared to visual images. Firstly, facial temperature measured by thermal camera does not depend on skin color, darkness, and lighting condition. Secondly, facial thermal images are changed not only by facial expression but also emotional state. To our knowledge, there is no study to concurrently investigate these two sources of facial temperature changes. Method: 231 students participated in the experiment. Four kinds of stimuli inducing anger, fear, boredom, and neutral were presented to participants and the facial temperatures were measured by an infrared camera. Each stimulus consisted of baseline and emotion period. Baseline period lasted during 1min and emotion period 1~3min. In the data analysis, the temperature differences between the baseline and emotion state were analyzed. Eyes, mouth, and glabella were selected for facial expression features, and forehead, nose, cheeks were selected for emotional state features. Results: The temperatures of eyes, mouth, glanella, forehead, and nose area were significantly decreased during the emotional experience and the changes were significantly different by the kind of emotion. The result of linear discriminant analysis for emotion recognition showed that the correct classification percentage in four emotions was 62.7% when using both facial expression features and emotional state features. The accuracy was slightly but significantly decreased at 56.7% when using only facial expression features, and the accuracy was 40.2% when using only emotional state features. Conclusion: Facial expression features are essential in emotion recognition, but emotion state features are also important to classify the emotion. Application: The results of this study can be applied to human-computer interaction system in the work places or the automobiles.

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

Recently, emotion recognition systems based on physiological signals have introduced in humancomputer interaction researches. The aim of this study is to classify seven emotions (happiness, sadness, anger, fear, disgust, surprise, and stress) by machine learning algorithms using physiological signals. 12 college students participated in this experiment over 10 times. Total 70 emotional stimuli (10 emotional stimuli per each emotion) had been tested their suitability and effectiveness prior to experiment. Physiological signals, i.e. EDA, ECG, PPG, and SKT were acquired and were analyzed. Physiological signals were obtained prior to the presentation of emotional stimuli and while emotional stimuli were presented to participants. 28 features were extracted the acquired signals and analyzed for 30 seconds from the baseline and the emotional states. For emotion recognition, the data which is subtracted baseline values from the emotional state applied to 5 machine learning algorithm, i.e. FLD, CART, SOMs, Naive Bayes and SVM. The result showed that an accuracy of emotion classification by SVM was highest and lowest by FLD. This means that SVM is the best emotion recognition algorithm in this study. Our result can help emotion recognition studies lead to better chance to recognize not only basic emotion but also user’s various emotions, e.g., boredom, frustration, love, pain, etc., by using physiological signals. Also, it is able to be applied on many human-computer interaction devices for emotion detection.

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

Objective: This study is to review three representative psychological perspectives that explain scientific construct of emotion, that are the discrete emotion model, appraisal model, and dimensional model. Background: To develop emotion sensitive interface is the fusion area of emotion and scientific technology, it is necessary to have a balanced mixture of both the scientific theory of emotion and practical engineering technology. Extensional theories of the emotional structure can provide engineers with relevant knowledge in functional application of the systems. Method: To achieve this purpose, firstly, literature review on the basic emotion model and the circuit model of discrete emotion model as well as representative theories was done. Secondly, review on the classical and modern theories of the appraisal model emphasizing cognitive appraisal in emotion provoking events was conducted. Lastly, a review on dimensional theories describing emotion by dimensions and representative theories was conducted. Results: The paper compared the three models based on the prime points of the each model. In addition, this paper also made a comment on a need for a comprehensive model an alternative to each model, which is componential model by Scherer(2001) describing numerous emotional aspects. Conclusion: However, this review suggests a need for an evolved comprehensive model taking consideration of social context effect and discrete neural circuit while pinpointing the limitation of componential model. Application: Insight obtained by extensive scientific research in human emotion can be valuable in development of emotion sensitive interface and emotion recognition technology.

This study aimed to investigate the differences in brain functions during verbal working memory between individuals with alcohol use disorders (AUD) and normal controls. fMRI was used to scan brain activations associated with verbal working memory while participants performed 2-back and 0-back tasks. A total of 21 young male college students participated in the study. Eleven of those who clinically met the criteria for AUD were assigned to the AUD group, whereas ten demographically similar subjects who were social drinkers but not AUD were assigned to the normal control group. The AUD group showed less activation in bilateral frontal and precentral, left superior temporal, left superior parietal, and left cerebellar cortex during the 2-back task relative to 0-back task compared to the normal control group. In contrast, the control group showed less activation only in the right uncus than the AUD group. These results suggest that subjects with AUD present abnormality in brain functioning during verbal working memory.

This study was conducted to whether the administration of mexiletine, a Na+ channel blocker, impacts the recovery from demyelination. Under anesthesia, 0.1% ethidium bromide was injected into the dorsal funiculus (T3), followed by a mexiletine or saline treatment. Motor evoked potential (MEP) recordings and luxol fast blue stainings were performed at one, seven, 14, and 21 days post-operatively. Conduction was delayed during demyelination, but the mexiletine-injected group demonstrated shortened latencies and reductions in the demyelination area when compared to the control. These results suggest that systemic mexiletine plays a positive role in protecting neural tissues from demyelination.

Recent advances in optical imaging techniques have made it possible to monitor neural activity and provided powerful tools to reveal the spatiotemporal patterns of neural activity. We used optical imaging to determine whether nerve injury affects excitability of the sensory cortex. Male Sprague-Dawley rats were subjected to neuropathic surgery consisting of a tight ligation and transection of the left tibial and sural nerves while under pentobarbital anesthesia. The rats were reanesthetized with urethane two weeks post-operatively, and the exposed cortex surfaces were stained with a voltage-sensitive dye (di-2-ANEPEQ). After electrical stimulation of the receptive field, optical signals from the cerebral cortex were recorded using an optical imaging system. Increased optical intensity and an enlarged area of activation were observed in the cerebral cortex of neuropathic rats during electrical stimulation compared to normal or sham-operated rats. Higher electric stimulation resulted in more intensity and a larger area of activation in neuropathic rats. These results suggest that cortical excitability, resulting from peripheral stimulation, may be affected by nerve injury, which indicates a degree of neural plasticity.

The purpose of this study was to measure the average corpus callosum volume of normal Koreans (aged in their 20s or 40s) and to analyze the effects of gender, age, and body parameters, such as height and weight on corpus callosum size. Magnetic resonance brain images were recorded for 68 people in their 20s (29 men, 39 women) and 91 in their 40s (36 men, 55 women). Intracranial volume was calculated using cerebral size and corpus callosum volume was normalized by covariance method. To investigate the effect of gender and age on corpus callosum volume, two-way analysis of variance, which used gender (two levels) and age (two levels) as independent variables, was employed. Multiple regression analysis was carried out to investigate the effect of body parameters, such as height and weight, according to the age and gender on the changes in corpus callosum volume. The average corpus callosum volume of Korean men (11.09 cm(3)) was larger than that of Korean women (9.61 cm(3)). There was no significant difference in corpus callosum volume between 20s (10.43 cm(3)) and 40s (10.12 cm(3)). There was a positive relationship between body weight and corpus callosum volume for 20s, but not for 40s.

During exposure to light with different spectral compositions, the non-visual neural system in the brain gives rise to altered various physiological aspects that are not caused by the well-known primary visual pathway. This study has been designed to investigate non-visual information processing that affects the biological rhythms in humans by studying the properties of paraventricular neurons in Sprague-Dawley rats under urethane anesthesia. The responses of paraventricular nucleus neurons by stimulation with different wavelengths of light were recorded. It turned out that neural responses were most sensitive to 550 nm wavelength light. This result suggests that a non-visual system in the hypothalamus including the paraventricular nucleus responds to light stimuli to control biological rhythms or autonomic functions.

This study investigated the effect of 30% oxygen inhalation on visuospatial cognitive performance, blood oxygen saturation, and heart rate. Six male (25.8(mean)±1.0(SD) years) and six female (23.8±1.9 years) college students participated in this experiment. Two psychological tests were developed to measure the performance level of visuospatial cognition. The experiment consisted of two runs: one was a visuospatial cognition task under normal air (21% oxygen) condition and the other under hyperoxic air (30% oxygen) condition. The experimental sequence in each run consisted of four phases, that were Rest1 (1 min), Control (1 min), Task (4 min), and Rest2 (4 min). Blood oxygen saturation and heart rate were measured throughout the course of four phases. The analysis of behavioral performance with 30% oxygen administration when compared to 21% oxygen revealed that the mean performance was improved. When supplied 30% oxygen in the air, the blood oxygen saturation was increased while the heart rate was decreased compared to those under 21% oxygen condition. We conclude that 30% oxygen inhalation enhanced visuospatial performance by the increased the oxygen saturation in the blood.

Studies on the characteristics of individuals with hypersexual disorder have been accumulating due to increasing concerns about problematic hypersexual behavior (PHB). Currently, relatively little is known about the underlying behavioral and neural mechanisms of sexual desire. Our study aimed to investigate the neural correlates of sexual desire with event-related functional magnetic resonance imaging (fMRI). Twenty-three individuals with PHB and 22 age-matched healthy controls were scanned while they passively viewed sexual and nonsexual stimuli. The subjects' levels of sexual desire were assessed in response to each sexual stimulus. Relative to controls, individuals with PHB experienced more frequent and enhanced sexual desire during exposure to sexual stimuli. Greater activation was observed in the caudate nucleus, inferior parietal lobe, dorsal anterior cingulate gyrus, thalamus, and dorsolateral prefrontal cortex in the PHB group than in the control group. In addition, the hemodynamic patterns in the activated areas differed between the groups. Consistent with the findings of brain imaging studies of substance and behavior addiction, individuals with the behavioral characteristics of PHB and enhanced desire exhibited altered activation in the prefrontal cortex and subcortical regions. In conclusion, our results will help to characterize the behaviors and associated neural mechanisms of individuals with PHB.

As imaging technology develops, magnetic resonance imaging (MRI) has furthered our understanding of brain function by clarifying the anatomical structure and generating functional imaging data related to information processing in pain conditions. Recent studies have reported that manganese (Mn(2+))-enhanced MRI (MEMRI) provides valuable information about the functions of the central nervous system. The aim of this study was to identify specific brain regions activated during noxious electric stimulation using high-resolution MEMRI. Male Sprague Dawley rats were divided into three groups: naive, sham electrical stimulation, and noxious electric stimulation. Under urethane with α-chloralose mixture anesthesia, a catheter was placed in the external carotid artery to administrate 20% mannitol and manganese chloride (25mM MnCl2). Noxious electric stimulation (2Hz, 10V) was applied to the hind paw with a needle electrode. Stimulation-induced neuronal activation was detected using 4.7-T MRI. In response to noxious electrical stimulation, remarkable Mn(2+)-enhanced signals were observed in the agranular insular cortex, auditory cortex, primary somatosensory cortex of the hind limb, and granular and dysgranular insular cortex, which correspond to sensory tactile electric stimulus to the hindpaws. These results indicate that the combination of MEMRI with activity-induced Mn(2+)-dependent contrast can delineate functional areas in the rat brain.

Background: Sexual behavior is an important role for the survival of species. The advancement of brain imaging methods has enabled the understanding of the brain mechanism related to sexual arousal. The previous studies on the brain mechanism related to sexual arousal have mostly conducted on block design paradigm. Methods: Despite its requirement for stricter experimental control, the event-related paradigm is known to be more efficient in detecting instant emotional and cognitive responses. The paradigm also enables the observation of hemodynamic responses through time. Therefore, this study used the event-related fMRI to examine the brain activation in various areas associated with sexual arousal as well as changes in hemodynamic responses with time. Results: Strong activations were observed in the various areas associated with sexual arousal comprised of various factors: (1) activation areas related to cognitive factors: the occipital lobe and parietal lobe; (2) activation areas related to emotional factors: the thalamus and amygdala; (3) activation areas related to motivational factors: the anterior cingulate gyrus, orbitofrontal cortex, and insula; and (4) activation areas related to physiological factors: the precentral gyrus, putamen, and globus pallidus. We also identified the activation of the putamen and globus pallidus that were not well observed in previous block design studies. In the result of the hemodynamic response, the neural activity in those areas showed more transient aspects of the hemodynamic responses relative to the neural activity of other areas. Conclusions: These results suggested that the event-related paradigm is better at detecting the neural activity of the brain regions, which tend to appear suddenly, but disappear soon.

Background Alcoholism is associated with abnormal anger processing. The purpose of this study was to investigate brain regions involved in the evaluation of angry facial expressions in patients with alcohol dependency. Methods Brain blood-oxygenation-level-dependent (BOLD) responses to angry faces were measured and compared between patients with alcohol dependency and controls. Results During intensity ratings of angry faces, significant differences in BOLD were observed between patients with alcohol dependency and controls. That is, patients who were alcohol-dependent showed significantly greater activation in several brain regions, including the dorsal anterior cingulate cortex (dACC) and medial prefrontal cortex (MPFC). Conclusions Following exposure to angry faces, abnormalities in dACC and MPFC activation in patients with alcohol dependency indicated possible inefficiencies or hypersensitivities in social cognitive processing.

The purpose of the present study is to develop a magnetic resonance (MR)-compatible electrocardiogram (ECG) amplifier. If ECG signals are measured simultaneously with the acquisition of MR images, there can be a mutual interference effect. The present system was designed to block noises caused by the main magnetic field, gradient magnetic field and radiofrequency (RF) pulse when MR images are acquired. It uses analogue elements in order to remove any possible effect on MR images. In addition, a radio-frequency-interference (RFI)-free optical data link using the pulse-width modulation (PWM) technique is adopted in order to transmit ECG signals measured inside the MR room.

This study aimed to investigate the hypothesis that administration of the air with 30% oxygen compared with normal air (21% oxygen) enhances cognitive functioning through increased activation in the brain. A visuospatial task was presented while brain images were scanned by a 3 T fMRI system. The results showed that there was an improvement in performance and also increased activation in several brain areas in the higher oxygen condition. These results suggest that a higher concentration of breathed oxygen increases saturation of blood oxygen in the brain and facilitates performance.

Functional deficits after spinal cord injury have originated not only from the direct physical damage itself, but from the secondary biochemical and pathological changes. Apoptotic cell death has been seen around the periphery of an injured site and has been known to ultimately progress to necrosis and infarction. We have initiated the present study focusing on the role of apoptosis in the secondary injury of the brain after acute spinal cord injury (SCI), and conducted a series of experiments, the study examining the morphological changes in the brain following the spinal injury. Under pentobarbital anesthesia, male Sprague–Dawley rats were subjected to SCI model. Rats were laminectomized and SCI was induced using NYU spinal impactor at T9 segment. The behavioral test was performed. Electrophysiologically, motor evoked potentials (MEPs) were recorded. The animals were subjected to morphological study at 12, 24, 48, 72 h, and 1 week, postoperatively. Locomotor deficits were observed after SCI, and changes in the amplitudes and latencies of the MEPs were observed. The morphological changes were evidenced by terminal TUNEL staining and Calbindin-D 28K immunohistochemistry. The TUNEL-positive cells were located at the brain motor cortex after SCI. TUNEL-positive cells were seldom found 4 h after injury. In addition, Calbindin-D 28K immunoreactive neurons were observed in the motor cortex after injury. These results suggest that apoptosis may play an important role in the pathophysiology of the brain motor cortex following acute spinal cord injury and functions that were deteriorated after SCI may be related to these electrophysiological and morphological changes.

The localization of brain areas related to cognitive functions has yet to be thoroughly explored in children. We therefore set out to apply volumetric, voxel-based, and structural connectivity analyses to magnetic resonance images from a large sample of healthy children. We could confirm a strong correlation of whole-brain gray matter volume and the individual intelligence quotient; however, this correlation only developed with age in our sample, in that it was not present in the younger children. With the application of an optimized protocol for voxel-based morphometry, the anterior cingulate was shown to be directly correlated with a measure of human intelligence. Furthermore, an analysis of structural connectivity identified gray matter volume in several distinct brain areas to be related to cognitive functions. The implications of our findings for normal development, pathological processes, and our understanding of cognition are discussed and related to previous findings.

The most crucial feature of human computer interaction is computers and computer-based applications to infer the emotional states of humans or others human agents based on covert and/or overt signals of those emotional states. In emotion recognition, bio-signals reflect sequences of neural activity induced by emotional events and also, have many technical advantages. The aim of this study is to classify six emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multi-channel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmograph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using five algorithms, linear discriminant analysis, Naive Bayes, classification and regression tree, self-organization map and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 42.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naive Bayes and linear discriminant analysis were highest (53.9%, 52.7%) and was lowest by support vector machine (39.2%). This means that Naive Bayes is the best emotion recognition algorithm for basic emotions. To apply to real system, we have to discuss in the view point of testing and this means that it needs to apply various methodologies for the accuracy improvement of emotion recognition in the future analysis.

Background Humor is composed of a cognitive element related to the detection of humor and an affective element related to the appreciation of humor. To investigate activated areas of the brain related to the two components of humor and to identify neural substrates associated with the degree of humor intensity, 13 participants were scanned while watching cartoons. Findings While watching humorous scenes, various areas of the brain were activated, including (1) the inferior gyrus, an area involved in reconciling ambiguous semantic content with stored knowledge, and (2) the temporal gyrus and fusiform gyrus, brain regions associated with the feeling of mirth. Further, humor intensity was positively correlated with BOLD signal magnitude in the nucleus accumbens, a region known to be involved in psychologically and psychopharmacologically driven rewards. Conclusions Our findings demonstrate a two-component neural circuit model of humor processing and a key region important in pleasurable feelings accompanied by humor.

Emotions are complex processes involving multiple response channels, including physiological systems, facial expressions and voices. Bio-signals reflect sequences of neural activity, which result in changes in autonomic and neuroendocrine systems induced by emotional events. Therefore in human-computer interaction researches, one of the most current interesting topics in emotion recognition is to recognize human's feeling using bio-signals. The aim of this study is to classify emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multichannel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmo-graph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using four algorithms, linear discriminant analysis, Naive Bayes, classification and regression tree and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 56.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naive Bayes was highest (53.9%) and lowest by support vector machine (39.2%). This means that Naive Bayes is the best emotion recognition algorithm for basic emotions. This result can be helpful to provide the basis for the emotion recognition technique in human-computer interaction.

Extraversion is a personality frequently discussed as one of the strongest and most consistent factors that relates to individual subjective wellbeing. The goal of this study was to better understand how people with varying degrees of extraversion psychologically and physiologically respond differently to unpleasant circumstances. Emotional responses (e.g., levels of intensity, valence, and arousal) were assessed in determining the sensitivity level to negative stimuli that were specifically designed to provoke physical pain and sadness emotion. Physiological changes (e.g., heart rate (HR), blood volume pulse (BVP), and respiratory sinus arrhythmia (RSA)) were also measured during pain and sadness to observe sympathetic and parasympathetic activities. Our results showed that the degree of extraversion was associated with less unpleasant responses to sadness, less HR responses to both pain and sadness, and greater RSA responses to sadness. The findings suggest that the lower HR reactivity to painful and sad situations and greater RSA reactivity to sad situations in extraversion could be possibly due to increased parasympathetic activity. Additionally, enhanced parasympathetic activity to negative situations may explain an important mechanism underlying the positive connection between extraversion and subjective wellbeing.

This study is related with emotion recognition based on autonomic nervous system responses. Three different emotional states, fear, surprise and stress, are evoked by stimuli and the autonomic nervous system responses for the induced emotions are measured as physiological signals such as skin temperature, electrodermal activity, electrocardiogram, and photoplethysmography. Twenty-eight features are analysed and extracted from these signals. The results of one-way ANOVA toward each parameter, there are significant differences among three emotions in some features. Therefore we select eight features from 28 features for emotion recognition. The comparative results of emotion recognition are discussed in view point of feature space with the selected features. For emotion recognition, we use four machine learning algorithms, namely, linear discriminant analysis, classification and regression tree, self-organizing map and naive bayes, and those are evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. This can be helpful to provide the basis for the emotion recognition technique in human computer interaction as well as contribute to the standardization in emotion-specific ANS responses.

It has been well documented that there is opioid resistance in neuropathic pain. This indicates that the endogenous opioid system may not be involved effectively in modulating neuropathic pain. The present study sought to determine if activation of the descending pain inhibition system might produce analgesia in the animal neuropathic model we developed. Under ketamine anesthesia, male Sprague-Dawley rats were chronically implanted with stimulating electrodes in the ventral periaqueductal gray matter (PAG) and both the tibial and sural nerves of the sciatic nerve branches were severed. Pain sensitivity was measured with a von Frey filament and acetone applied to the sensitive area for 1 week postoperatively. Rats with neuropathic pain syndrome after transection of the tibial and sural nerves were tested as to the analgesic effects of ventral PAG stimulation for an additional two weeks. Electrical stimulation of the ventral PAG turned out to be highly effective in alleviating neuropathic pain. Mechanical allodynia and cold allodynia were reduced by PAG stimulation. Naloxone reversed the antiallodynic effects of ventral PAG stimulation. These results suggest that activation of the descending pain inhibition system including the ventral PAG reduces neuropathic pain syndrome and that opiates are involved in this system.

We have previously demonstrated that electrical stimulation of the ventral periaqueductal gray matter (PAG) produced analgesia in neuropathic pain in rats. Opioids were also shown to be involved in analgesic effects. This study sought to determine whether opiates microinjected into the ventral PAG produce analgesia. Male Sprague-Dawley rats were chronically implanted with a guide cannula in the PAG under pentobarbital anesthesia and both the tibial and sural nerves were completely cut. Pain sensitivity was postoperatively measured with a von Frey filament and acetone applied to the sensitive area for I week. Opioids such as [D-Ala 2 ,N-MePhe 4 ,Gly(ol) 5 ]-enkephalin (DAMGO) and [D-Pen 2 ,D-Pen 5 ].-enkephalin (DPDPE) were injected into the PAG. DAMGO, a μ-opioid agonist, and DPDPE, a δ-opioid agonist, were highly effective in reducing neuropathic pain. These effects were reversed by naloxone. These results suggest that the neurons in the ventral PAG are activated by opioids to produce analgesia and that specific opioid receptors are involved in the descending pain inhibition system from the PAG.

The average cerebellar volume of Korean men (135.19 cm3) is larger than that of Korean women (123.06 cm3), and that of subjects in their twenties (134.28 cm3) is larger than that of subjects in their forties (121.83 cm3). Atrophy of the cerebellum is more markedly observed in men than in women. There is a relation between body weight and cerebellar volume for men, but not for women.

It is difficult to classify anger, fear, and surprise emotions with autonomic nervous system response patterns, because these three emotions show similar levels of valence and arousal dimensions. The purpose of this study was to classify three emotions by using EEG signals. Linear discriminant analysis (LDA) using three types of EEG characteristics showed that the mean recognition accuracy was 66.3%. These findings reveal that three emotions were successfully able to be classified based on EEG signals.

The purpose of this study is to identify optimal algorithm for emotion classification which classify seven different emotional states (happiness, sadness, anger, fear, disgust, surprise, and stress) using physiological features. Skin temperature, photoplethysmography, electrodermal activity and electrocardiogram are recorded and analyzed as physiological signals. The emotion stimuli used to induce a participant's emotion are evaluated for their suitability and effectiveness. For classification problems of seven emotions, the design involves two main phases. At the first phase, Particle Swarm Optimization selects P % of patterns to be treated as prototypes of seven emotional categories. At the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative elements of the original feature space. The study offers a complete algorithmic framework and demonstrates the effectiveness of the approach for a collection of selected data sets.

Recently, the one of main topic of emotion recognition or classification research is to recognize human's feeling or emotion using various physiological signals. It is one of the core processes to implement emotional intelligence in human computer interaction (HCI) research. The purpose of this study was to identify the best algorithm being able to discriminate negative emotions, such as sadness, fear, surprise, and stress using physiological features. Electrodermal activity (EDA), electrocardiogram (ECG), skin temperature (SKT), and photoplethysmography (PPG) are recorded and analyzed as physiological signals. And emotional stimuli used in this study are audio-visual film clips which have examined for their appropriateness and effectiveness through preliminary experiment. For classification of negative emotions, five machine learning algorithms, i.e., LDF, CART, SOM, Naive Bayes and SVM are used. Result of emotion classification shows that an accuracy of emotion classification by SVM (100.0%) was the highest and by LDA (50.7%) was the lowest. CART showed emotion classification accuracy of 84.0%, SOM was 51.2% and Naive Bayes was 76.2%. This can be helpful to provide the basis for the emotion recognition technique in HCI.

This study aimed to investigate the differences in brain functions during verbal working memory between individuals with alcohol use disorders (AUD) and normal controls. fMRI was used to scan brain activations associated with verbal working memory while participants performed 2-back and 0-back tasks. A total of 21 young male college students participated in the study. Eleven of those who clinically met the criteria for AUD were assigned to the AUD group, whereas ten demographically similar subjects who were social drinkers but not AUD were assigned to the normal control group. The AUD group showed less activation in bilateral frontal and precentral, left superior temporal, left superior parietal, and left cerebellar cortex during the 2-back task relative to 0-back task compared to the normal control group. In contrast, the control group showed less activation only in the right uncus than the AUD group. These results suggest that subjects with AUD present abnormality in brain functioning during verbal working memory.

FK506 has been originally classified as an immunosuppressant and is known to exhibit neurotrophic actions in vitro and protective effects on some neurological conditions. We investigated the neuroprotective effects of FK506 on kainic acid (KA)-induced neuronal death in organotypic hippocampal slice cultures (OHSCs). After an 18 h KA (5 microM) treatment, significantly neuronal death was detected in the CA3 region using propidium iodide staining. However, neuronal death was significantly prevented at 24 and 48 h after treatment with 0.1 microM FK506. Using cresyl violet staining, we also observed that an increased number of CA3 neurons survived in the 0.1 microM FK506 group compared to the KA only group. Based on the results of the Western blot analysis, the expressions of 5-lipoxygenase and caspase-3 were reduced 24h after 0.1 microM FK506 treatment. The levels of superoxide dismutase (SOD) and phospho-Akt expression were increased by treatment with 0.1 microM FK506. These results suggest that FK506 may have a positive role in protecting neurons against cell death in the KA injury model of OHSCs.

This study aimed to investigate the hypothesis that a transient increase in oxygen level administered to subjects increases the BOLD effect in brain regions associated with verbal cognitive functioning and enhances performance accuracy. A verbal task was presented while brain images were scanned by a 3T fMRI system. The accuracy rate on the verbal task was enhanced during 30% oxygen administration compared to 21% oxygen administration. The neural activations were observed at the occipital, parietal, temporal and frontal lobes, during both 21% and 30% oxygen administration. Increased brain activations were observed in the right middle frontal gyrus, right inferior frontal gyrus, right superior frontal gyrus, cingulate gyrus, left middle temporal gyrus, and left fusiform gyrus with 30% oxygen administration. These results suggest that a higher concentration of breathed oxygen increases saturation of blood oxygen in the brain, and facilitates verbal cognitive performance.

This study investigated what effect a 30% oxygen administration had on visuospatial cognitive performance and cerebral activation and lateralization using fMRI. Eight college students were selected as the subjects for this study. An oxygen dispenser that provided 21% and 30% oxygen at a constant rate of 8L/min was developed. In order to measure the performance level of visuospatial cognition, two psychological tests were also developed. The experiment consisted of two runs, one for a visuospatial cognition task with normal air (21% oxygen) and the other for a visuospatial cognition task with hyperoxic air (30% oxygen). Functional brain images were taken with a 3T MRI using the single-shot EPI method. The results of the visuospatial behavioral analysis reveal that accuracy rates were enhanced with 30% oxygen administration when compared to 21% oxygen. There were more activations observed at the bilateral occipital, parietal, and frontal lobes with 30% oxygen administration. However, decreased cerebrum lateralization was observed with 30% oxygen administration in the same regions compared with 21% oxygen administration. Thus, it is concluded that the positive effect on the visuospatial cognitive performance level by the highly concentrated oxygen administration resulted from an increase of cerebrum activation and a decrease of cerebrum lateralization.