Affective State Classification Through CMAC-Based Model of Affects (CCMA) Using SVM

2017 ◽  
Vol 23 (11) ◽  
pp. 11369-11373
Author(s):  
Hamwira Yaacob ◽  
Abdul Wahab
Keyword(s):  
Affective State ◽  
State Classification

A Portable Personality Recognizer Based on Affective State Classification Using Spectral Fusion of Features

2018 ◽  
Vol 9 (3) ◽  
pp. 330-342 ◽  
Author(s):  
Anushree Basu ◽  
Anirban Dasgupta ◽  
Anirud Thyagharajan ◽  
Aurobinda Routray ◽  
Rajlakshmi Guha ◽  
...  
Keyword(s):  
Affective State ◽  
State Classification

Affective State Classification in Virtual Reality Environments Using Electrocardiogram and Respiration Signals

2021 ◽  
Author(s):  
Apostolos Kalatzis ◽  
Laura Stanley ◽  
Vishnunarayan Girishan Prabhu
Keyword(s):  
Virtual Reality ◽  
Affective State ◽  
State Classification

scholarly journals Affective State Classification Using Bayesian Classifier

2014 ◽  
Author(s):  
Aimi Shazwani Ghazali ◽  
Shahrul Naim Sidek ◽  
Saodah Wok
Keyword(s):  
Affective State ◽  
Bayesian Classifier ◽  
State Classification

Associations Between Self-Report of Emotional State and the EEG Patterns in Affective Disorders Patients

2010 ◽  
Vol 24 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Jan Kaiser ◽  
Anton M. L. Coenen
Keyword(s):  
State Energy ◽  
Emotional State ◽  
Spectral Power ◽  
Bipolar Affective Disorder ◽  
Affective State ◽  
Manic Episode ◽  
High Energy ◽  
Self Report ◽  
Major Depressive ◽  
High Tension

The study determines the associations between self-report of ongoing emotional state and EEG patterns. A group of 31 hospitalized patients were enrolled with three types of diagnosis: major depressive disorder, manic episode of bipolar affective disorder, and nonaffective patients. The Thayer ADACL checklist, which yields two subjective dimensions, was used for the assessment of affective state: Energy Tiredness (ET) and Tension Calmness (TC). Quantitative analysis of EEG was based on EEG spectral power and laterality coefficient (LC). Only the ET scale showed relationships with the laterality coefficient. The high-energy group showed right shift of activity in frontocentral and posterior areas visible in alpha and beta range, respectively. No effect of ET estimation on prefrontal asymmetry was observed. For the TC scale, an estimation of high tension was related to right prefrontal dominance and right posterior activation in beta1 band. Also, decrease of alpha2 power together with increase of beta2 power was observed over the entire scalp.

Emotional Reactivity to Visual Content as Revealed by ERP Component Clustering

2015 ◽  
Vol 29 (4) ◽  
pp. 135-146 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Szczepan J. Grzybowski ◽  
Jan Kaiser
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Affective State ◽  
Occipital Lobe ◽  
Stimulus Onset ◽  
Emotional States ◽  
Neural Basis ◽  
Temporoparietal Junction ◽  
The Right ◽  
The Impact

Abstract. In the study, the neural basis of emotional reactivity was investigated. Reactivity was operationalized as the impact of emotional pictures on the self-reported ongoing affective state. It was used to divide the subjects into high- and low-responders groups. Independent sources of brain activity were identified, localized with the DIPFIT method, and clustered across subjects to analyse the visual evoked potentials to affective pictures. Four of the identified clusters revealed effects of reactivity. The earliest two started about 120 ms from the stimulus onset and were located in the occipital lobe and the right temporoparietal junction. Another two with a latency of 200 ms were found in the orbitofrontal and the right dorsolateral cortices. Additionally, differences in pre-stimulus alpha level over the visual cortex were observed between the groups. The attentional modulation of perceptual processes is proposed as an early source of emotional reactivity, which forms an automatic mechanism of affective control. The role of top-down processes in affective appraisal and, finally, the experience of ongoing emotional states is also discussed.

If You Can’t Stand the Heat . . . Take Off Your Coat: The Affective State of Groups and the Role of the Facilitator

PsycCRITIQUES ◽  
2010 ◽  
Vol 55 (46) ◽  
Author(s):  
Alisha Francis
Keyword(s):  
Affective State

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

The Automatic Method of EEG State Classification by Using Self-Organizing Map

2010 ◽  
Vol 130 (3) ◽  
pp. 420-427 ◽  
Author(s):  
Kazuhiro Tamura ◽  
Takamasa Shimada ◽  
Yoichi Saito
Keyword(s):  
Self Organizing Map ◽  
Automatic Method ◽  
State Classification ◽  
Self Organizing

Self organizing feature map and radial basis function based voltage stability state classification of power system

2013 ◽  
Vol 16 (1) ◽  
pp. 7-25 ◽  
Author(s):  
Kabir Chakraborty ◽  
Abhinandan De ◽  
Abhijit Chakrabarti
Keyword(s):  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Voltage Stability ◽  
Feature Map ◽  
State Classification ◽  
Radial Basis ◽  
Self Organizing
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