Emotional eye movement analysis using electrooculography signal

Paul, Sananda; Banerjee, Anwesha; Tibarewala, D. N.

doi:10.1504/ijbet.2017.082224

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…The least successful approaches utilized only pupil diameter achieving highly similar and low accuraries of 58.9% [42] and 59.0% [36], respectively. The most commonly used feature (eight studies) was pupil diameter [31,36,42,57,86,94,96,101], followed by fixation duration employed in four studies [31,73,84,101], and finally the least used features were pupil position [57,94] and EOG [53,54], which were used in only two studies each, respectively. The speed of the emotion recognition task was only reported in one of the studies, which could provide classification results within 2 s (with 10% variation) of the presentation of the emotional stimuli [94].…”

Section: Discussionmentioning

confidence: 99%

“…In Paul et al [54], the authors used the audio-visual stimulus to recognize emotion using EOG signals with the Hjorth parameter and a time-frequency domain feature extraction method, which was the Discrete Wavelet Transform (DWT) [55]. They used two classifiers in their study to obtain the classification which was SVM and Naïve Bayes (NB) with Hjorth [56].…”

Section: Electrooculography (Eog)mentioning

confidence: 99%

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Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Lim

Mountstephens

Teo

2020

Sensors

180

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The ability to detect users’ emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.

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Section: Discussionmentioning

confidence: 99%

Section: Electrooculography (Eog)mentioning

confidence: 99%

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Lim

Mountstephens

Teo

2020

Sensors

180

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“…The scientific research presented in the Table 11 embraces EOG technology for emotion recognition and their intensity evaluation. A majority of techniques separate positive and negative emotion levels (see [164][165][166]). The evaluation of emotion intensity level remains uncertain for many cases and not comprehensibly described.…”

Section: Electrooculography (Eog)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

406

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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“…However, studies that focus specifically on emotion classification using eye-tracking data alone is very limited as most of such studies incorporate other sensor modalities such as EEG and ECG. There is a study that focuses on the emotional eye movement analysis using electrooculography (EOG) signals [20]. There have also been studies that rely on other types of eyetracking data such as fixation duration and pupil position [1,23].…”

Section: Eye-tracking In Emotion Classificationmentioning

confidence: 99%

Four-class emotion classification in virtual reality using pupillometry

2020

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Background: Emotion classification remains a challenging problem in affective computing. The large majority of emotion classification studies rely on electroencephalography (EEG) and/or electrocardiography (ECG) signals and only classifies the emotions into two or three classes. Moreover, the stimuli used in most emotion classification studies utilize either music or visual stimuli that are presented through conventional displays such as computer display screens or television screens. This study reports on a novel approach to recognizing emotions using pupillometry alone in the form of pupil diameter data to classify emotions into four distinct classes according to Russell's Circumplex Model of Emotions, utilizing emotional stimuli that are presented in a virtual reality (VR) environment. The stimuli used in this experiment are 360° videos presented using a VR headset. Using an eye-tracker, pupil diameter is acquired as the sole classification feature. Three classifiers were used for the emotion classification which are Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and Random Forest (RF). Findings: SVM achieved the best performance for the four-class intra-subject classification task at an average of 57.05% accuracy, which is more than twice the accuracy of a random classifier. Although the accuracy can still be significantly improved, this study reports on the first systematic study on the use of eye-tracking data alone without any other supplementary sensor modalities to perform human emotion classification and demonstrates that even with a single feature of pupil diameter alone, emotions could be classified into four distinct classes to a certain level of accuracy. Moreover, the best performance for recognizing a particular class was 70.83%, which was achieved by the KNN classifier for Quadrant 3 emotions. Conclusion: This study presents the first systematic investigation on the use of pupillometry as the sole feature to classify emotions into four distinct classes using VR stimuli. The ability to conduct emotion classification using pupil data alone represents a promising new approach to affective computing as new applications could be developed using readily-available webcams on laptops and other mobile devices that are equipped with cameras without the need for specialized and costly equipment such as EEG and/or ECG as the sensor modality.

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Emotional eye movement analysis using electrooculography signal

Cited by 15 publications

References 12 publications

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Human Emotion Recognition: Review of Sensors and Methods

Four-class emotion classification in virtual reality using pupillometry

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