New computer interface combining gaze tracking and brainwave measurements

Bang, Jae Won; Lee, Eui Chul; Park, Kang Ryoung

doi:10.1109/tce.2011.6131137

Cited by 24 publications

(48 citation statements)

References 15 publications

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“…Figure 2(a) shows the gaze-tracking device [13,14]. It uses a commercial USB web camera (Logitech C600 web camera [15]) with a zoom lens in order to acquire a larger eye image.…”

Section: Proposed Device and Methodsmentioning

confidence: 99%

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

Choi

Bang

Park

et al. 2013

Sensors

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Speller UI systems tend to be less accurate because of individual variation and the noise of EEG signals. Therefore, we propose a new method to combine the EEG signals and gaze-tracking. This research is novel in the following four aspects. First, two wearable devices are combined to simultaneously measure both the EEG signal and the gaze position. Second, the speller UI system usually has a 6 × 6 matrix of alphanumeric characters, which has disadvantage in that the number of characters is limited to 36. Thus, a 12 × 12 matrix that includes 144 characters is used. Third, in order to reduce the highlighting time of each of the 12 × 12 rows and columns, only the three rows and three columns (which are determined on the basis of the 3 × 3 area centered on the user's gaze position) are highlighted. Fourth, by analyzing the P300 EEG signal that is obtained only when each of the 3 × 3 rows and columns is highlighted, the accuracy of selecting the correct character is enhanced. The experimental results showed that the accuracy of proposed method was higher than the other methods.

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“…Figure 2(a) shows the gaze-tracking device [13,14]. It uses a commercial USB web camera (Logitech C600 web camera [15]) with a zoom lens in order to acquire a larger eye image.…”

Section: Proposed Device and Methodsmentioning

confidence: 99%

“…As shown in Figure 2(b), four (custom-made) NIR illuminators are attached to the four corners of the monitor to produce four corneal specular reflections on the eye that represent the four monitor corners [13]. Twenty-one NIR light-emitting diodes (LEDs) are included in each NIR illuminator.…”

Section: Proposed Device and Methodsmentioning

confidence: 99%

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

Choi

Bang

Park

et al. 2013

Sensors

Self Cite

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show abstract

“…Equations (16) and (17) are estimated gaze point (Sx, Sy) x-axis deviation (E dx ) and y-axis deviation (E dy ) from gazed point (S') actual coordinates. Finally, Equation (18) gives the expression of Euclidean error E(S x,y ) between estimated gaze point and actual user screen coordinates.…”

Section: Initial Parametersmentioning

confidence: 99%

Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images

Durna

Arı

2017

Applied Sciences

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This study proposes a novel binocular pupil and gaze detection system utilizing a remote full high definition (full HD) camera and employing LabVIEW. LabVIEW is inherently parallel and has fewer time-consuming algorithms. Many eye tracker applications are monocular and use low resolution cameras due to real-time image processing difficulties. We utilized the computer's direct access memory channel for rapid data transmission and processed full HD images with LabVIEW. Full HD images make easier determinations of center coordinates/sizes of pupil and corneal reflection. We modified the camera so that the camera sensor passed only infrared (IR) images. Glints were taken as reference points for region of interest (ROI) area selection of the eye region in the face image. A morphologic filter was applied for erosion of noise, and a weighted average technique was used for center detection. To test system accuracy with 11 participants, we produced a visual stimulus set up to analyze each eye's movement. Nonlinear mapping function was utilized for gaze estimation. Pupil size, pupil position, glint position and gaze point coordinates were obtained with free natural head movements in our system. This system also works at 2046 × 1086 resolution at 40 frames per second. It is assumed that 280 frames per second for 640 × 480 pixel images is the case. Experimental results show that the average gaze detection error for 11 participants was 0.76 • for the left eye, 0.89 • for right eye and 0.83 • for the mean of two eyes.

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“…The NIR cutting filter of the camera is removed and a NIR passing filter is included in its place. 9,[14][15][16] The NIR light-emitting diode (LED) illuminator is positioned below the camera. In this way, we can capture the facial image of the user without any adverse effects from changes in the visible environmental light conditions.…”

Section: Proposed Gaze Detection Systemmentioning

confidence: 99%

“…7, the region of interest (ROI) for pupil detection can be defined as shown in Fig 9(a). The brightness of the ROI region is changed through a stretching of the histogram [step (2) 14,15,21 From the processed image achieved through the stretching of the histogram, we can obtain the maximum and minimum pixel values. The thresholds for binarization images 1 and 2 are determined based on the maximum and minimum pixel values, respectively, by which the binarized image can be robust .…”

Section: Pupil Center Detectionmentioning

confidence: 99%

Binocular gaze detection method using a fuzzy algorithm based on quality measurements

et al. 2014

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Abstract. Due to the limitations of gaze detection based on one eye, binocular gaze detection using the gaze positions of both eyes has been researched. Most previous binocular gaze detection research calculated a gaze position as the simple average position of the detected gaze points of both eyes. To improve this approach, we propose a new binocular gaze detection method using a fuzzy algorithm with quality measurement of both eyes. The proposed method is used in the following three ways. First, in order to combine the gaze points of the left and right eyes, we measure four qualities on both eyes: distortion by an eyelid, distortion by the specular reflection (SR), the level of circularity of the pupil, and the distance between the pupil boundary and the SR center. Second, in order to obtain a more accurate pupil boundary, we compensate the distorted boundary of a pupil by an eyelid based on information from the lower half-circle of the pupil. Third, the final gaze position is calculated using a fuzzy algorithm based on four quality-measured scores. Experimental results show that the root-mean-square error of gaze estimation by the proposed method is approximately 0.67518 deg. © 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.

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New computer interface combining gaze tracking and brainwave measurements

Cited by 24 publications

References 15 publications

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

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

Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images

Binocular gaze detection method using a fuzzy algorithm based on quality measurements

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