Estimating Emotion Changes Using Electroencephalographic Activities and its Clinical Application

Osaka, Kyoko; Chiba, Shuya; Tanioka, Tetsuya; Kawanishi, Chihiro; Nagamine, Isao; Ren, Fuji; Kuroiwa, Shingo; Tada, T.; Yamashita, Renata Paciello; M, Kishimoto; Nishimura, Masanobu; Yamamoto, Akira Y.; Locsin, Rozzano C.; Takasaka, Yoichiro

doi:10.1109/nlpke.2005.1598851

Cited by 3 publications

(1 citation statement)

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“…To be more precise, we analyze information contained in the brain wave [36], [37], sound voice, visual image and speech pattern from the perspective of mental features [34]. We also analyze large statistical data based on the latest result of neurology and psychology in order to derive a mental state transition network.…”

Section: Introductionmentioning

confidence: 99%

Affective Information Processing and Recognizing Human Emotion

Ren

2009

Electronic Notes in Theoretical Computer Science

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Information recognition and extraction of human emotions are necessary for machines to communicate smoothly with humans and to realize emotion communications. We focus on human psychological characteristics to develop general-purpose agents that can recognize human emotion and create machine emotion. We comprehensively analyze brain waves, voice sounds and picture images that represent information included in emotion elements of phonation, facial expressions, and speech usage. We analyze and estimate many statistical data based on the latest achievements of brain science and psychology in order to derive transition networks for human psychological states. We establish a speaker word model for researching computer simulation of psychological change and emotional presentation, developing emotion interface, and establishing theoretic structure and realization method of emotion communication. A new approach for recognizing human emotion based on Mental State Transition Network will be described and one emotion estimation method based on sentence pattern of emotion occurrence events will be discussed, and some new results of the project will be given.

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Section: Introductionmentioning

confidence: 99%

Affective Information Processing and Recognizing Human Emotion

Ren

2009

Electronic Notes in Theoretical Computer Science

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Emotional State Recognition Using Advanced Machine Learning Techniques on EEG Data

Giannakaki

Giannakakis

Farmaki

et al. 2017

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)

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Electroencephalograph Estimation Method of Measuring "Empathic Understanding"

Osaka

Tanioka²,

Locsin³

et al. 2007

2007 International Conference on Natural Language Processing and Knowledge Engineering

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IntroductionIt is very important for health care professionals, It is important for health care staff to unparticularly nurses to understand emotional empaderstand patients' emotion and affective thetic understanding. Travelbee believed that nurschanges. Health care staffs should undering is accomplished through human-to-human relastand the patients' world as if this was his tionships. She defined empathy as the ability to her own. This is called "empathic undershare the patient's experience wherein one is able standing". To understand the emotional to predict the behavior of the patient. She also deprocess, electroencephalograph recording fined sympathy as going beyond empathy and oc-(EEG) was performed on five healthy parcurring when the nurse desires to alleviate the ticipants while watching the sentimental cause of the patient's suffering and is "involved but movie which was 24-minutes long. The not incapacitated". The nurses reaching the stage EEG changes during the emotional scenes of sympathy would be desirable (Travelbee, 1966). were collected and compared with emoMoreover, clinical experiences with patients and tionally stable states. By using power map families tell nurses the importance of empathic and spectrogram analyses of EEG, alpha understanding.bands at the occipital region were seen speElectroencephalograph recording (EEG) is one cifically among the emotionally impressed of the instruments generally used in the hospital to states. It seemed that the changes indicated examine encephalopathy and other brain disorders. the objective expression of empathic un-EEG measures electrical changes in neural actividerstanding. We suggest that EEG with ties and indicates the area where, and when the power map and spectrogram analyses is a action potential occurs (Musha, 1997), particularly useful fundamental tool for developing a when using power map and spectrogram analyses second generation computer system adof EEG. The brain controls not only thoughts and justed to the changes of one's mind. Howactions but also emotional states and autonomic ever, further examinations to remove artichanges with emotion. Musha et al. (1998) explain facts among the EEG results are needed. that the state of mind is supported by the brain activity and hence features of the state of mind appear in the scalp potentials of BEG. Davidson has suggested that resting BEG activity reflects the joint contribution of traits that are superimposed 978-1-4244-161 O-3/07/$25.OO©C2007IEEE 514

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Estimating Emotion Changes Using Electroencephalographic Activities and its Clinical Application

Cited by 3 publications

References 8 publications

Affective Information Processing and Recognizing Human Emotion

Affective Information Processing and Recognizing Human Emotion

Emotional State Recognition Using Advanced Machine Learning Techniques on EEG Data

Electroencephalograph Estimation Method of Measuring "Empathic Understanding"

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