In the twinkling of an eye: Synchronization of EEG and eye tracking based on blink signatures

Abstract

ACHIEVING ROBUST ADAPTIVE SYNCHRONIZATION OF MULTIMODAL BIOMETRIC INPUTS: The recent arrival of wireless EEG headsets that enable mobile real-time 3D brain imaging on smartphones, and low cost eye trackers that provide gaze control of tablets, will radically change how biometric sensors might be integrated into next generation user interfaces. In experimental lab settings EEG neuroimaging and eye tracking data are traditionally combined using external triggers to synchronize the signals. However, with biometric sensors increasingly being applied in everyday usage scenarios, there will be a need for solutions providing a continuous alignment of signals. In the present paper we propose using spontaneous eye blinks, as a means to achieve near real-time synchronization of EEG and eye tracking. Analyzing key parameters that define eye blink signatures across the two domains, we outline a probability function based algorithm to correlate the signals. Comparing the accuracy of the method against a state of the art EYE-EEG plug-in for offline analysis of EEG and eye tracking data, we propose our approach could be applied for robust synchronization of biometric sensor data collected in a mobile context.

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

“…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.…”

Near-Zero Phase-Lag Hyperscanning in a Novel Wireless EEG System

“…Several studies [9,17,18] have used an external trigger to synchronize the multiple data streams. Baekgaard et al [17] employed spontaneous eye-blinking signatures to align EEG signals with eye-tracking patterns. Artoni et al [19] delivered a digital input through a transistortransistor logic (TTL) port to EEG and electromyography (EMG) devices simultaneously, discovering a misalignment of ±5 ms and jitter of 1.7 ms in a 10-min recording.…”

“…Bækgaard et al . [17] employed spontaneous eye-blinking signatures to align EEG signals with eye-tracking patterns. Artoni et al .…”

“…EEG hyperscanning has become increasingly popular, but the synchronization method used in EEG hyperscanning has not yet been standardized. Several studies [9,17,18] have used an external trigger to synchronize the multiple data streams. Baekgaard et al [17] employed spontaneous eye-blinking signatures to align EEG signals with eye-tracking patterns.…”

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

“…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.…”