A crucial temporal accuracy test of combining EEG and Tobii eye tracker

Xue, Jiguo; Cheng, Qimin; Li, Chunyong; Yue, Jingwei; Zhang, Chenggang

doi:10.1097/md.0000000000006444

Cited by 16 publications

(12 citation statements)

References 36 publications

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“…Nowadays, these issues are largely solved by optimizing the laboratory setup and by sending shared trigger pulses to both systems during each trial (e.g., Baccino & Manunta, 2005 ). The two recordings can then be aligned offline at millisecond precision with existing software solutions (e.g., the EYE-EEG toolbox) ( Dimigen et al, 2011 ; see also Baekgaard, Petersen, & Larsen, 2014 ; Xue, Quan, Li, Yue, & Zhang, 2017 ) that also add saccade and fixation onsets as additional event markers to the EEG.…”

Section: Introductionmentioning

confidence: 99%

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Dimigen

Ehinger

2021

Journal of Vision

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Section: Introductionmentioning

confidence: 99%

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Dimigen

Ehinger

2021

Journal of Vision

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“…The other problems relate to the (1) integration of eye-track and EEG, (2) the temporally varying overlap between the neural responses generated by successive fixations, and (3) the complex influences of visual and oculomotor low-level variables (such as saccade size) on the morphology of the post-saccadic lambda waves. The first of these problem can now be solved with dedicated toolboxes (such as EYE-EEG, see also Baekgaard, Petersen, & Larsen, 2014;Xue, Quan, Li, Yue, & Zhang, 2017), whereas the latter two are effectively addressed by analyzing the artifact-corrected EEG with regression-based linear deconvolution models (Burns, Bigdely-Shamlo, Smith, Kreutz-Delgado, & Makeig, 2013;Dandekar, Privitera, Carney, & Klein, 2011;Ehinger & Dimigen, 2018;Kristensen, Rivet, & Guérin-Dugué, 2017;Smith & Kutas, 2015). If ocular correction is also optimized, there are now viable solutions to all four problems.…”

Section: Other Challenges When Analyzing Multi-saccadic Eegmentioning

confidence: 99%

Optimized ICA-based removal of ocular EEG artifacts from free viewing experiments

Dimigen¹,

Berlin²

2018

Preprint

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Author Note. I would like to acknowledge Maarten De Schuymer, who conducted early explorations of the effects of high-pass filtering that helped to initiate the current work. I am also grateful to Lisa Spiering for assisting with the MSEC correction and Werner Sommer for providing an excellent working environment. Collection of one of the datasets was supported by a grant from DFG (FG-868-A2). Comments or corrections are highly appreciated. ICA of free viewing EEG ABSTRACTCombining EEG with eye-tracking is a promising approach to study neural correlates of natural vision, but the resulting recordings are also heavily contaminated by activity of the eye balls, eye lids, and extraocular muscles. While Independent Component Analysis (ICA) is commonly used to suppress these ocular artifacts, its performance under free viewing conditions has not been systematically evaluated and many published findings display residual artifacts. Here I evaluated and optimized ICA-based correction for two tasks with unconstrained eye movements: visual search in images and sentence reading. In a first step, four parameters of the ICA pipeline were systematically varied: the (1) high-pass and (2) low-pass filter applied to the training data, (3) the proportion of training data containing myogenic saccadic spike potentials (SP), and (4) the threshold for eye tracker-based component rejection. In a second step, the eyetracker was used to objectively quantify correction quality of each ICA solution, both in terms of undercorrection (residual artifacts) and overcorrection (removal of neurogenic activity). As a benchmark, results were compared to those obtained with an alternative spatial filter, Multiple Source Eye Correction (MSEC). With commonly used settings, Infomax ICA not only left artifacts in the data of both tasks, but also distorted neurogenic activity during eye movementfree intervals. However, correction could be drastically improved by training the ICA on optimally filtered data in which SPs were massively overweighted. With optimized procedures, ICA removed virtually all artifacts, including the SP and its associated spectral broadband artifact, with little distortion of neural activity. It also outperformed MSEC in terms of SP correction. Matlab code is provided.

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“…For locating the target object, in this paper, we utilized the human gaze movement which is unimpaired for the targeted patients. Scientists have already tried two approaches to measure EEG and eye Movement simultaneously [ 26 ]. In the first approach, EOG was recorded with EEG to detect eye movements [ 27 ], whereas in the second approach eye movement was recognized through an eye tracker [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Scientists have already tried two approaches to measure EEG and eye Movement simultaneously [ 26 ]. In the first approach, EOG was recorded with EEG to detect eye movements [ 27 ], whereas in the second approach eye movement was recognized through an eye tracker [ 26 ]. Though the last approach is providing better accuracy, the wearable EOG goggles are preferred more for Human–Computer Interaction (HCI) purposes [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

An Electro-Oculogram Based Vision System for Grasp Assistive Devices—A Proof of Concept Study

Roy

Mahadevappa

Nazarpour

2021

Sensors

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Humans typically fixate on objects before moving their arm to grasp the object. Patients with ALS disorder can also select the object with their intact eye movement, but are unable to move their limb due to the loss of voluntary muscle control. Though several research works have already achieved success in generating the correct grasp type from their brain measurement, we are still searching for fine controll over an object with a grasp assistive device (orthosis/exoskeleton/robotic arm). Object orientation and object width are two important parameters for controlling the wrist angle and the grasp aperture of the assistive device to replicate a human-like stable grasp. Vision systems are already evolved to measure the geometrical attributes of the object to control the grasp with a prosthetic hand. However, most of the existing vision systems are integrated with electromyography and require some amount of voluntary muscle movement to control the vision system. Due to that reason, those systems are not beneficial for the users with brain-controlled assistive devices. Here, we implemented a vision system which can be controlled through the human gaze. We measured the vertical and horizontal electrooculogram signals and controlled the pan and tilt of a cap-mounted webcam to keep the object of interest in focus and at the centre of the picture. A simple ‘signature’ extraction procedure was also utilized to reduce the algorithmic complexity and system storage capacity. The developed device has been tested with ten healthy participants. We approximated the object orientation and the size of the object and determined an appropriate wrist orientation angle and the grasp aperture size within 22 ms. The combined accuracy exceeded 75%. The integration of the proposed system with the brain-controlled grasp assistive device and increasing the number of grasps can offer more natural manoeuvring in grasp for ALS patients.

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A crucial temporal accuracy test of combining EEG and Tobii eye tracker

Abstract: Supplemental Digital Content is available in the text

Cited by 16 publications

References 36 publications

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Optimized ICA-based removal of ocular EEG artifacts from free viewing experiments

An Electro-Oculogram Based Vision System for Grasp Assistive Devices—A Proof of Concept Study

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