Easy-setup eye movement recording system for human-computer interaction

Phung, Manh Duong; Trần, Quang Vĩnh; Hara, Keishiro; Inagaki, Hiroki; Abe, Mitsuru

doi:10.1109/rivf.2008.4586369

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Another method may be to try and reduce the complexity of the CNN's architecture as was suggested by Lotte et.al 2018 [6]. Either trials could in turn improve the model's ability to produce a higher and more consistent rate of accuracy, ultimately allowing the CNN model to be used to control a device or combine with other inputs of human [13] to carry out more complicated tasks.…”

Section: Discussionmentioning

confidence: 99%

Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

Gallo¹,

Phung²

2022

Preprint

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Objective A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data. Approach PyCharm, a Python platform, will be used to house and process the CNN. The raw data used for the training of the CNN will be sourced from the PhysioBank website. The EEG signal data will then be pre-processed using Brainstorm software that is a toolbox used in conjunction with MATLAB. The sample data used to validate and test the trained CNN, will be also be extracted from Brainstorm but in a much smaller size compared to the training data which is comprised of thousands of images. The sample size would be comparable to a person wearing a Brain Computer Interface (BCI), offering approximately 20 seconds of motor imagery signal data. Results The raw EEG data was successfully extracted and pre-processed. The deep learning model was trained using the extracted image data along with their corresponding labels. After training, it was able to accurately identify the T1 class label at 100 percent. The python coding was then modified to restore the trained model and feed it test sample data in which it was found to recognise 6 out of 10 lines of T1 signal image data. The result suggested that the initial training of the model required a different, more varying approach, so that it would be able to detect varying sample signal image data. The outcome of which could mean that the model could be used in applications for multiple patients wearing the same BCI hardware to control a device or interface.

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Section: Discussionmentioning

confidence: 99%

Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

Gallo¹,

Phung²

2022

Preprint

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“…In this paper, the eye gaze tracking solution could be implemented in a pure hardware way. Compared with the PC-based and DSP-based solutions [2][3] , the proposed solution could reduce the CPU / GPU load and reduce system power consumption and cost. Compared with the FPGAbased solutions [4][5] , the proposed solution had a faster eye gaze tracking speed (the eye gaze tracking speed of paper [4] is 62.5Hz and the speed of paper [5] is 11Hz).…”

Section: Impactmentioning

confidence: 99%

30‐2: Implementation of a Real‐time Eye Gaze Tracking Solution for ASIC Based on VR Display

Yang

Sun

Gao

et al. 2018

Symp Digest of Tech Papers

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“…A method for object tracking and detection is developed in [10] for maritime surface vehicle navigation using stereo vision system to locate objects as well as calculating the distance from the target object in the harsher maritime environment. A methodology of human computer interaction to compute eye movement by detecting the eye corner and the pupil center using visual digital signal processor camera is invented in [11]. The mentioned novel approach helps the users to move their head freely without wiring any external gadgets.…”

Section: Introductionmentioning

confidence: 99%

Vision Tracking: A Survey of the State-of-the-Art

et al. 2020

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Vision tracking is a well-studied framework in vision computing. Developing a robust visual tracking system is challenging because of the sudden change in object motion, cluttered background, partial occlusion and camera motion. In this study, the state-of-the art visual tracking methods are reviewed and different categories are discussed. The overall visual tracking process is divided into four stages-object initialization, appearance modeling, motion estimation, and object localization. Each of these stages is briefly elaborated and related researches are discussed. A rapid growth of visual tracking algorithms is observed in last few decades. A comprehensive review is reported on different performance metrics to evaluate the efficiency of visual tracking algorithms which might help researchers to identify new avenues in this area. Various application areas of the visual tracking are also discussed at the end of the study.

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Easy-setup eye movement recording system for human-computer interaction

Cited by 5 publications

References 10 publications

Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

30‐2: Implementation of a Real‐time Eye Gaze Tracking Solution for ASIC Based on VR Display

Vision Tracking: A Survey of the State-of-the-Art

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