A real-time classification algorithm for emotion detection using portable EEG

Cheemalapati, Surya; Gubanov, Michael; Vale, Michael Del; Pyayt, Anna

doi:10.1109/iri.2013.6642541

Cited by 16 publications

(13 citation statements)

References 7 publications

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“…Na podstawie pomiaru fal Beta, Delta oraz Theta (Rys. 5) udało im się uzyskać ostrzeżenia o wystąpieniu lęku w momencie, gdy tylko badany zaczynał odczuwać strach w reakcji na wyświetlany obraz [8].…”

Section: Algorytm Klasyfikacji Emocji Zapomocą Przenośnego Eegunclassified

Analysis of possibility of virtual reality usage for investigating reaction on given conditions

Łukasiak¹,

Machul²,

Maj³

2019

jcsi

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The study was tend to checking the possibility of using VR technology to invoke specific emotions on the subject. Literature studies have shown a high probability of obtaining successful results, but so far studies have been based on measurements obtained using specialized, expensive equipment. This study was about to show that obtaining similar results is also possible with usage of low budget solutions. Received results allow to conclude even using the less sophisticated technology, satisfactory results can be gained, which can determine the effect of the image displayed in the goggles on the emotions of the subject.

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Section: Algorytm Klasyfikacji Emocji Zapomocą Przenośnego Eegunclassified

Analysis of possibility of virtual reality usage for investigating reaction on given conditions

Łukasiak¹,

Machul²,

Maj³

2019

jcsi

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“…The electrical potential is nothing but the voltage between pairs of these electrodes which are then filtered, amplified, and recorded. Brain waves have been classified into four different frequency bands [21]: delta (0-4 Hz), theta (4-8 Hz), alpha (812 Hz) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30).…”

Section: Emotion Recognition Based On Eegmentioning

confidence: 99%

Emotion Recognition Based on Various Physiological Signals - A Review

Londhe¹,

Borse²

2018

IJCT

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Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition.

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“…Single modality-based methods include visible light camera-based methods [8][9][10][11][12][13][14][15], thermal camera-based methods [16,17], voice-based methods [2,[12][13][14]18,19], physiological signal-based methods [20][21][22][23][24][25], and functional magnetic resonance imaging (fMRI)-based methods [26]. As for visible light camera-based methods, images of subjects' facial expression at different emotions are captured by a camera and the generated data used to study the information on subjects' facial expression (changes in facial feature points by comparison between previous image frames and present image frames, etc.)…”

Section: Related Workmentioning

confidence: 99%

“…However, they also have a disadvantage in that the level of accuracy of the instruments varies greatly depending on their price level. Hence, the use of low-cost instruments may undermine the credibility of data in a study [20][21][22][23][24][25]28]. In the case of the fMRI-based method, the anger felt by study subjects was recognized through fMRI, and it was demonstrated that it is possible to analyze the response to fear by studying neural responses to anger [26].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy System-Based Fear Estimation Based on the Symmetrical Characteristics of Face and Facial Feature Points

2017

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Abstract:The application of user emotion recognition for fear is expanding in various fields, including the quantitative evaluation of horror movies, dramas, advertisements, games, and the monitoring of emergency situations in convenience stores (i.e., a clerk threatened by a robber), in addition to criminal psychology. Most of the existing methods for the recognition of fear involve referring to a single physiological signal or recognizing circumstances in which users feel fear by selecting the most informative one among multiple physiological signals. However, the level of accuracy as well as the credibility of these study methods is low. Therefore, in this study, data with high credibility were obtained using non-intrusive multimodal sensors of near-infrared and far-infrared light cameras and selected based on t-tests and Cohen's d analysis considering the symmetrical characteristics of face and facial feature points. The selected data were then combined into a fuzzy system using the input and output membership functions of symmetrical shape to ultimately derive a new method that can quantitatively show the level of a user's fear. The proposed method is designed to enhance conventional subjective evaluation (SE) by fuzzy system based on multi-modalities. By using four objective features except for SE and combining these four features into a fuzzy system, our system can produce an accurate level of fear without being affected by the physical, psychological, or fatigue condition of the participants in SE. After conducting a study on 20 subjects of various races and genders, the results indicate that the new method suggested in this study has a higher level of credibility for the recognition of fear than the methods used in previous studies.Keywords: fear; multimodal sensors of near-infrared and far-infrared light cameras; symmetrical characteristics of face and facial feature points; fuzzy system; input and output membership functions of symmetrical shape

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A real-time classification algorithm for emotion detection using portable EEG

Cited by 16 publications

References 7 publications

Analysis of possibility of virtual reality usage for investigating reaction on given conditions

Analysis of possibility of virtual reality usage for investigating reaction on given conditions

Emotion Recognition Based on Various Physiological Signals - A Review

Fuzzy System-Based Fear Estimation Based on the Symmetrical Characteristics of Face and Facial Feature Points

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