Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

Mala, Shuchi; Latha, K.

doi:10.1155/2014/713818

Cited by 16 publications

(7 citation statements)

References 26 publications

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“…This section will present the investigations that have been reported in the literature for the extraction of useful features from eye-tracking data for emotion classification. As an example, in the study of Mala et al [30], the authors report on the use of optimization techniques for feature selection based on a differential evolution algorithm in an attempt to maximize the emotional recognition rates. Differential evolution is a process that optimizes the solution by iteratively attempting to improve the candidate solution for a given quality measure and it keeps the best score for the solution.…”

Section: Emotional-relevant Features From Eye-trackingmentioning

confidence: 99%

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

Lim

Mountstephens

Teo

2020

Sensors

182

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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: Emotional-relevant Features From Eye-trackingmentioning

confidence: 99%

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

Lim

Mountstephens

Teo

2020

Sensors

182

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“…For those with frail grip strength, an alternative wheelchair interface is needed, which can be helped by human-machine interfaces (HMI). Multiple studies have demonstrated HMI applications (Aziz et al 2014; Barea et al 2002a; Belkacem et al 2015; Keegan et al 2009; Wu et al 2013) and activity tracking (Bulling et al 2009; Mala and Latha 2014) via non-invasive electrooculograms (EOG). EOG signals are derived from the potential that is created by the eye acting as a dipole through the positively charged cornea and negatively charged retina.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms to enable multi-class differentiation use various features for maximized accuracy of subject-dependent signals. The continuous wavelet transform, Haar wavelet, is one of the most widely used features for EOG recognition (Aziz et al 2014; Belkacem et al 2015; Bulling et al 2009; Mala and Latha 2014). For example, this feature was included in a classification method (Bulling et al 2011) for multiple task recognitions such as copy, read, write, video, browse, and null.…”

Section: Introductionmentioning

confidence: 99%

Soft, conformal bioelectronics for a wireless human-wheelchair interface

Mishra

Norton

Yong-Kuk

et al. 2017

Biosensors and Bioelectronics

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There are more than 3 million people in the world whose mobility relies on wheelchairs. Recent advancement on engineering technology enables more intuitive, easy-to-use rehabilitation systems. A human-machine interface that uses non-invasive, electrophysiological signals can allow a systematic interaction between human and devices; for example, eye movement-based wheelchair control. However, the existing machine-interface platforms are obtrusive, uncomfortable, and often cause skin irritations as they require a metal electrode affixed to the skin with a gel and acrylic pad. Here, we introduce a bioelectronic system that makes dry, conformal contact to the skin. The mechanically comfortable sensor records high-fidelity electrooculograms, comparable to the conventional gel electrode. Quantitative signal analysis and infrared thermographs show the advantages of the soft biosensor for an ergonomic human-machine interface. A classification algorithm with an optimized set of features shows the accuracy of 94% with five eye movements. A Bluetooth-enabled system incorporating the soft bioelectronics demonstrates a precise, hands-free control of a robotic wheelchair via electrooculograms.

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“…Several human-machine interface technologies utilize horizontal EOG signal, which has been used as a control strategy for a mobility aid [29]. Feature extraction techniques have been performed for analysis of the signal with minimum redundancy and maximum relevant features to improve the communication based on the EOG signal for disabled people [30][31][32][33]. Generally, for EOG signal acquisition techniques, a three-electrode system has been employed for communication and control purposes of assistive devices [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Study of electrooculography signal acquisition sites for assistive device applications

Raj¹

2021

IJATEE

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Electrooculography (EOG) is a technique that involves the measurement of the corneo-retinal standing potential of the eye. The human eye acts as a dipole between the cornea (positive potential) and the retina (negative potential), creating an electric field around the eyeball. The resulting electric signal obtained from this field is called electrooculogram. These signals, generated by eye movements, could be measured by employing different electrode placement configurations for the acquisition. The properties of these signals change depending on the number and placement of the electrodes. The study conducted here describes EOG signal acquisition using new electrode placement configurations that employ fewer facial electrodes placed on the patient. Three pre-gelled disposable electrodes were utilized for this purpose. Only one electrode was placed on a facial location, enhancing patient comfort during the acquisition procedure. To support this study, a lowcost signal acquisition hardware was developed. Using active filtering and amplification, appropriate signal processing techniques were executed upon the horizontal EOG signal acquired to reduce noise and interference due to external conditions. Hence, this paper presents the findings of new electrode placement sites for the acquisition of EOG signals which could be used for assistive device applications while restricting the number of facial electrodes to one. Most of the studies regarding the EOG signal acquisition had been using all electrodes in the face region. In contrast, we reduced the number of electrodes in the face region, thereby providing patient comfort. The comparison was made mainly on the acquired data from these new locations to discover the configuration with the optimal signal response using data obtainedfrom seven healthy subjects. The amplitude values were being compared from the new locations with the standard acquisition sites. The findings of this study were found to have a productive result. The total gain of the system required for new electrode placement configurations was two times more than the total gain required for standard acquisition sites, and also, the amplitude was less but can be helpful for assistive device applications. The average peak to peak amplitude value of the EOG signal for the new site came around about 1.25 volt.

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Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

Cited by 16 publications

References 26 publications

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

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

Soft, conformal bioelectronics for a wireless human-wheelchair interface

Study of electrooculography signal acquisition sites for assistive device applications

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