skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Imperceptible electrooculography graphene sensor system for human–robot interface
Abstract Electrooculography (EOG) is a method to record the electrical potential between the cornea and the retina of human eyes. Despite many applications of EOG in both research and medical diagnosis for many decades, state-of-the-art EOG sensors are still bulky, stiff, and uncomfortable to wear. Since EOG has to be measured around the eye, a prominent area for appearance with delicate skin, mechanically and optically imperceptible EOG sensors are highly desirable. Here, we report an imperceptible EOG sensor system based on noninvasive graphene electronic tattoos (GET), which are ultrathin, ultrasoft, transparent, and breathable. The GET EOG sensors can be easily laminated around the eyes without using any adhesives and they impose no constraint on blinking or facial expressions. High-precision EOG with an angular resolution of 4° of eye movement can be recorded by the GET EOG and eye movement can be accurately interpreted. Imperceptible GET EOG sensors have been successfully applied for human–robot interface (HRI). To demonstrate the functionality of GET EOG sensors for HRI, we connected GET EOG sensors to a wireless transmitter attached to the collar such that we can use eyeball movements to wirelessly control a quadcopter in real time.  more » « less
Award ID(s):
1738293
PAR ID:
10154418
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
npj 2D Materials and Applications
Volume:
2
Issue:
1
ISSN:
2397-7132
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Accurate and continuous monitoring of eye movements using compact, low‐power‐consuming, and easily‐wearable sensors is necessary in personal and public health and safety, selected medical diagnosis techniques (point‐of‐care diagnostics), and personal entertainment systems. In this study, a highly sensitive, noninvasive, and skin‐attachable sensor made of a stable flexible piezoelectric thin film that is also free of hazardous elements to overcome the limitations of current computer‐vision‐based eye‐tracking systems and piezoelectric strain sensors is developed. The sensor fabricated from single‐crystalline III‐N thin film by a layer‐transfer technique is highly sensitive and can detect subtle movements of the eye. The flexible eye movement sensor converts the mechanical deformation (skin deflection by eye blinking and eyeball motion) with various frequencies and levels into electrical outputs. The sensor can detect abnormal eye flickering and conditions caused by fatigue and drowsiness, including overlong closure, hasty eye blinking, and half‐closed eyes. The abnormal eyeball motions, which may be the sign of several brain‐related diseases, can also be measured, as the sensor generates discernable output voltages from the direction of eyeball movements. This study provides a practical solution for continuous sensing of human eye blinking and eyeball motion as a critical part of personal healthcare, safety, and entertainment systems. 
    more » « less
  2. Abstract Faces are salient social stimuli that attract a stereotypical pattern of eye movement. The human amygdala and hippocampus are involved in various aspects of face processing; however, it remains unclear how they encode the content of fixations when viewing faces. To answer this question, we employed single-neuron recordings with simultaneous eye tracking when participants viewed natural face stimuli. We found a class of neurons in the human amygdala and hippocampus that encoded salient facial features such as the eyes and mouth. With a control experiment using non-face stimuli, we further showed that feature selectivity was specific to faces. We also found another population of neurons that differentiated saccades to the eyes vs. the mouth. Population decoding confirmed our results and further revealed the temporal dynamics of face feature coding. Interestingly, we found that the amygdala and hippocampus played different roles in encoding facial features. Lastly, we revealed two functional roles of feature-selective neurons: 1) they encoded the salient region for face recognition, and 2) they were related to perceived social trait judgments. Together, our results link eye movement with neural face processing and provide important mechanistic insights for human face perception. 
    more » « less
  3. Abstract Humans detect faces efficiently from a young age. Face detection is critical for infants to identify and learn from relevant social stimuli in their environments. Faces with eye contact are an especially salient stimulus, and attention to the eyes in infancy is linked to the emergence of later sociality. Despite the importance of both of these early social skills—attending to faces and attending to the eyes—surprisingly little is known about how they interact. We used eye tracking to explore whether eye contact influences infants' face detection. Longitudinally, we examined 2‐, 4‐, and 6‐month‐olds' (N = 65) visual scanning of complex image arrays with human and animal faces varying in eye contact and head orientation. Across all ages, infants displayed superior detection of faces with eye contact; however, this effect varied as a function of species and head orientation. Infants were more attentive to human than animal faces and were more sensitive to eye and head orientation for human faces compared to animal faces. Unexpectedly, human faces with both averted heads and eyes received the most attention. This pattern may reflect the early emergence of gaze following—the ability to look where another individual looks—which begins to develop around this age. Infants may be especially interested in averted gaze faces, providing early scaffolding for joint attention. This study represents the first investigation to document infants' attention patterns to faces systematically varying in their attentional states. Together, these findings suggest that infants develop early, specialized functional conspecific face detection. 
    more » « less
  4. Li-Jessen, Nicole Yee-Key (Ed.)
    The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model’s prediction accuracy on the Earable device’s classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings. 
    more » « less
  5. Scalp electroencephalography (EEG) is a neural source signal that is extensively used in neuroengineering due to its non-invasive nature and ease of collection. However, a drawback to the use of EEG is the prevalence of physiological artifacts generated by eye movements and eye blinks that contaminate the brain signals. Previously, we have proposed and validated an H ∞ -based Adaptive Noise Cancellation (ANC) technique for the real-time identification, learning and removal of eye blinks, eye motions, amplitude drifts and recording biases from EEG simultaneously. However, the standard electroocu- lography (EOG) electrode configuration requires four elec- trodes for EOG measurement, which limits its applicability for reduced-channel mobile applications, such as brain-computer interfaces (BCI). Here, we assess multiple configurations with varying number of EOG electrodes and compare the ANC effectiveness of these configurations to the ideal four-electrode configuration. From an analysis of the root mean squared error (RMSE) and differences in signal to noise ratios (SNR) between the ideal four-electrode case and the alternative configurations, it is reported that several three-electrode alternative configu- rations were effective in essentially replicating the ability to remove EOG artifacts in an experimental cohort of ten healthy subjects. For nine subjects, it was shown that only two to three EOG electrodes were needed to achieve similar performance as compared to the four-electrode case. This study demonstrates that the typical four-electrode configuration for EOG recordings for adaptive noise cancellation of ocular artifacts may not be necessary; by using the proposed new EOG configurations it is possible to improve electrode allocation efficiency for EOG measurements in mobile EEG applications. 
    more » « less