Enhanced Perception of User Intention by Combining EEG and Gaze-Tracking for Brain-Computer Interfaces (BCIs)

Choi, JongSuk; Bang, Jae Won; Park, Kang Ryoung; Whang, Mincheol

doi:10.3390/s130303454

Cited by 23 publications

(32 citation statements)

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“…These reflections are located by binarization, component labeling, and size filtering. 9 The four specular reflections represent the rectangular area of the display. Therefore, on the basis of the detected pupil center and the four specular reflections, the user's gaze position on the display is calculated according to the geometric transform between the rectangle formed by the four reflections and the rectangle of the display.…”

Section: Gaze Tracking Methodsmentioning

confidence: 99%

“…9,11 Using the captured eye image, the pupil's center is detected on the basis of circular edge detection, local binarization, component labeling, size filtering, filling of the specular reflection area, and calculation of the geometric center of the remaining black pixels as the pupil center.…”

Section: Gaze Tracking Methodsmentioning

confidence: 99%

“…8,[9][10][11] The eye-capturing camera is attached to an eyeglass frame near the lower part of one eye, as shown in Fig. 1.…”

Section: Device For Measuring Gaze Position and Eyementioning

confidence: 99%

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Minimizing eyestrain on a liquid crystal display considering gaze direction and visual field of view

et al. 2013

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Abstract. Recently, it has become necessary to evaluate the performance of display devices in terms of human factors. To meet this requirement, several studies have been conducted to measure the eyestrain of users watching display devices. However, these studies were limited in that they did not consider precise human visual information. Therefore, a new eyestrain measurement method is proposed that uses a liquid crystal display (LCD) to measure a user's gaze direction and visual field of view. Our study is different in the following four ways. First, a user's gaze position is estimated using an eyeglass-type eye-image capturing device. Second, we propose a new eye foveation model based on a wavelet transform, considering the gaze position and the gaze detection error of a user. Third, three video adjustment factors-variance of hue (VH), edge, and motion information-are extracted from the displayed images in which the eye foveation models are applied. Fourth, the relationship between eyestrain and three video adjustment factors is investigated. Experimental results show that the decrement of the VH value in a display induces a decrease in eyestrain. In addition, increased edge and motion components induce a reduction in eyestrain.

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Section: Gaze Tracking Methodsmentioning

confidence: 99%

Section: Gaze Tracking Methodsmentioning

confidence: 99%

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Minimizing eyestrain on a liquid crystal display considering gaze direction and visual field of view

et al. 2013

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“…A commercial Emotiv EPOC headset is used as the EEG measuring device, which is comprised of 16 electrodes with two reference nodes (CMS and DRL, as shown in Figure 3) [17]. The electrodes are positioned based on the international 10–20 system of electrode placement, also shown in Figure 3 [5,18]. The Emotiv EPOC system outputs the processed EEG data by using a built-in digital 5th order Sinc filter.…”

Section: Proposed System and Methodsmentioning

confidence: 99%

Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

Bang

Choi

Park

2013

Sensors

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Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.

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“…21 However, since only the approximate position and size of the iris region can be estimated by the RED algorithm, we perform CED to detect the iris position and size accurately. 27,29 The iris region is determined at the position where the maximum difference between the gray levels of the inner and outer boundary points of the circular template (whose radius is changeable) is obtained. 27 (1) should be selected corresponding to the Z distance.…”

Section: Estimating the Iris Region Of Nfov Image By Using Geometric mentioning

confidence: 99%

Improved iris localization by using wide and narrow field of view cameras for iris recognition

2013

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Biometrics is a method of identifying individuals by their physiological or behavioral characteristics. Among other biometric identifiers, iris recognition has been widely used for various applications that require a high level of security. When a conventional iris recognition camera is used, the size and position of the iris region in a captured image vary according to the X , Y positions of a user's eye and the Z distance between a user and the camera. Therefore, the searching area of the iris detection algorithm is increased, which can inevitably decrease both the detection speed and accuracy. To solve these problems, we propose a new method of iris localization that uses wide field of view (WFOV) and narrow field of view (NFOV) cameras. Our study is new as compared to previous studies in the following four ways. First, the device used in our research acquires three images, one each of the face and both irises, using one WFOV and two NFOV cameras simultaneously. The relation between the WFOV and NFOV cameras is determined by simple geometric transformation without complex calibration. Second, the Z distance (between a user's eye and the iris camera) is estimated based on the iris size in the WFOV image and anthropometric data of the size of the human iris. Third, the accuracy of the geometric transformation between the WFOV and NFOV cameras is enhanced by using multiple matrices of the transformation according to the Z distance. Fourth, the searching region for iris localization in the NFOV image is significantly reduced based on the detected iris region in the WFOV image and the matrix of geometric transformation corresponding to the estimated Z distance. Experimental results showed that the performance of the proposed iris localization method is better than that of conventional methods in terms of accuracy and processing time. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Subject terms: iris recognition; iris localization; wide field of view camera; narrow field of view camera; geometric transform.

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Enhanced Perception of User Intention by Combining EEG and Gaze-Tracking for Brain-Computer Interfaces (BCIs)

Cited by 23 publications

References 22 publications

Minimizing eyestrain on a liquid crystal display considering gaze direction and visual field of view

Minimizing eyestrain on a liquid crystal display considering gaze direction and visual field of view

Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

Improved iris localization by using wide and narrow field of view cameras for iris recognition

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