Tracking gaze position from EEG: Exploring the possibility of an EEG‐based virtual eye‐tracker
IntroductionOcular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (...
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| Published in | Brain and behavior Vol. 13; no. 10; pp. e3205 - n/a |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Los Angeles
John Wiley & Sons, Inc
01.10.2023
John Wiley and Sons Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-3279 2162-3279 |
| DOI | 10.1002/brb3.3205 |
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| Abstract | IntroductionOcular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement–related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position.MethodsAs proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.ResultsThe prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).ConclusionThis work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement. |
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| AbstractList | We present the first prototype of an EEG‐based virtual eye‐tracker achieving an accuracy of 0.920° and precision of 1.510° of visual angle in a representative participant, with an average accuracy of 1.008 ± 0.357? and precision of 2.348 ± 0.580? of visual angle across all participants. This work offers a novel approach that readily co‐registers eye movement and neural signals from EEG recording. Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement-related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position.INTRODUCTIONOcular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement-related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position.As proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.METHODSAs proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.The prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).RESULTSThe prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).This work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement.CONCLUSIONThis work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement. Abstract Introduction Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second‐order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)‐based identification method, capable of identifying and extracting eye movement–related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG‐derived SOBI components may be used to build predictive models for tracking gaze position. Methods As proof of this new concept, we designed an EEG‐based virtual eye‐tracker (EEG‐VET) for tracking eye movement from EEG alone. The EEG‐VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions. Results The prototype of EEG‐VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18). Conclusion This work offers a novel approach that readily co‐registers eye movement and neural signals from a single‐EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement. IntroductionOcular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement–related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position.MethodsAs proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.ResultsThe prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).ConclusionThis work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement. |
| Author | Privitera, Adam J. Tang, Akaysha C. Gao, Junling Cheng, Andy S. K. Hsiao, Janet Fong, Ching‐hang Chan, Cynthia Ding, Ruoxi Sun, Rui |
| AuthorAffiliation | 5 China Center for Health Development Studies Peking University Beijing China 4 Centre of Buddhism Studies The University of Hong Kong Hong Kong SAR China 1 Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong SAR China 2 Department of Psychology The University of Hong Kong Hong Kong SAR China 7 Centre for Research and Development in Learning Nanyang Technological University Singapore 3 The Laboratory of Neuroscience for Education The University of Hong Kong Hong Kong SAR China 6 Neural Dialogue Shenzhen China |
| AuthorAffiliation_xml | – name: 4 Centre of Buddhism Studies The University of Hong Kong Hong Kong SAR China – name: 5 China Center for Health Development Studies Peking University Beijing China – name: 6 Neural Dialogue Shenzhen China – name: 3 The Laboratory of Neuroscience for Education The University of Hong Kong Hong Kong SAR China – name: 1 Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong SAR China – name: 7 Centre for Research and Development in Learning Nanyang Technological University Singapore – name: 2 Department of Psychology The University of Hong Kong Hong Kong SAR China |
| Author_xml | – sequence: 1 givenname: Rui orcidid: 0000-0002-3205-7509 surname: Sun fullname: Sun, Rui organization: Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong SAR China, The Laboratory of Neuroscience for Education The University of Hong Kong Hong Kong SAR China – sequence: 2 givenname: Andy S. K. surname: Cheng fullname: Cheng, Andy S. K. organization: Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong SAR China – sequence: 3 givenname: Cynthia surname: Chan fullname: Chan, Cynthia organization: Department of Psychology The University of Hong Kong Hong Kong SAR China – sequence: 4 givenname: Janet surname: Hsiao fullname: Hsiao, Janet organization: Department of Psychology The University of Hong Kong Hong Kong SAR China – sequence: 5 givenname: Adam J. surname: Privitera fullname: Privitera, Adam J. organization: Centre for Research and Development in Learning Nanyang Technological University Singapore – sequence: 6 givenname: Junling surname: Gao fullname: Gao, Junling organization: Centre of Buddhism Studies The University of Hong Kong Hong Kong SAR China – sequence: 7 givenname: Ching‐hang surname: Fong fullname: Fong, Ching‐hang organization: Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong SAR China – sequence: 8 givenname: Ruoxi surname: Ding fullname: Ding, Ruoxi organization: China Center for Health Development Studies Peking University Beijing China – sequence: 9 givenname: Akaysha C. orcidid: 0000-0003-1158-5377 surname: Tang fullname: Tang, Akaysha C. organization: The Laboratory of Neuroscience for Education The University of Hong Kong Hong Kong SAR China, Neural Dialogue Shenzhen China |
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| Snippet | IntroductionOcular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research.... Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use... We present the first prototype of an EEG‐based virtual eye‐tracker achieving an accuracy of 0.920° and precision of 1.510° of visual angle in a representative... Abstract Introduction Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied... |
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| SubjectTerms | Accuracy Algorithms BSS Electroencephalography eye movement Eye movements high‐density EEG ICA Original Performance evaluation saccade SOBI Software |
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| Title | Tracking gaze position from EEG: Exploring the possibility of an EEG‐based virtual eye‐tracker |
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