This OSF contains the data for the paper 'Detection of Alcohol Inebriation from Eye Movements using Remote and Wearable Eye Trackers'. The raw data can be found in the folder raw_data (each zip file contains recorded data up- /downsampled to 1,000 Hz as csv-files). - Each csv file contains the recording (remote and wearable) for one subject for one PVT trial. - Each csv file contains the following columns: trial_id: trial-id for current recording block_id: block-id for current recording x_pix_eyelink: x-pixel coordinates using eyelink remote eye-tracker y_pix_eyelink: y-pixel coordinates using eyelink remote eye-tracker eyelink_timestamp: timestamp or recording in ms x_pix_pupilcore_interpolated: x-pixel coordinates using pupil-core eye-tracker upsampled to 1,000 Hz y_pix_pupilcore_interpolated: y-pixel coordinates using pupil-core eye-tracker upsampled to 1,000 Hz pupil_size_eyelink: pupil-size of pupil using eyelink remote eye-tracker target_distance: distance to eyelink remote eye-tracker (screen) in mm pupil_size_pupilcore_interpolated: pupil-size of pupil pupil-core eye-tracker upsampled to 1,000 Hz pupil_confidence_interpolated: pupil detection confidence of pupil pupil-core eye-tracker upsampled to 1,000 Hz time_to_prev_bac: elapsed time from previous BAC testing in ms time_to_next_bac: remaining time for next BAC testing in ms prev_bac: previous BAC concentration next_bac: next BAC concentration For more details see: https://github.com/aeye-lab/etra-potsdam-binge-pvt

Eye movements can be used to analyze a viewer’s cognitive capacities or mental state. Neural networks that process the raw eye-tracking signal can outperform methods that operate on scan paths preprocessed into fixations and saccades. However, the scarcity of such data poses a major challenge. We therefore develop SP-EyeGAN, a neural network that generates synthetic raw eye-tracking data. SP-EyeGAN consists of Generative Adversarial Networks; it produces a sequence of gaze angles indistinguishable from human ocular micro- and macro-movements. We explore the use of these synthetic eye movements for pre-training neural networks using contrastive learning. We find that pre-training on synthetic data does not help for biometric identification, while results are inconclusive for the detection of ADHD and gender classification. However, for the eye movement-based assessment of higher-level cognitive skills such general reading comprehension, text comprehension, and the distinction of native from non-native readers, pre-training on synthetic eye-gaze data improves the models’ performance and even advances the state-of-the-art for reading comprehension. The SP-EyeGAN model, pre-trained on GazeBase, along with the code for developing your own raw eye-tracking machine learning model with contrastive learning, is available at https://github.com/aeye-lab/sp-eyegan.

Neural networks that process the raw eye-tracking signal can outperform traditional methods that operate on scanpaths preprocessed into fixations and saccades. However, the scarcity of such data poses a major challenge. We, therefore, present SP-EyeGAN, a neural network that generates synthetic raw eye-tracking data. SP-EyeGAN consists of Generative Adversarial Networks; it produces a sequence of gaze angles indistinguishable from human micro- and macro-movements. We demonstrate how the generated synthetic data can be used to pre-train a model using contrastive learning. This model is fine-tuned on labeled human data for the task of interest. We show that for the task of predicting reading comprehension from eye movements, this approach outperforms the previous state-of-the-art.

In this repository we provide the Binge / PVT data set, extracted gaze features for the data set and the code to replicate the results presented in the paper "Detection of Alcohol Inebriation from Eye Movements ". The raw data set contains binocular gaze recordings and eye closure signals of 44 subjects, aged 18 to 47 with mean age 24. Each participant is recorded over 3 experimental sessions, with a time lag of at least one week between two sessions. [Entire raw data set will be added upon acceptance.]

Gaze patterns are known to be highly individual, and therefore eye movements can serve as a biometric characteristic. We explore aspects of the fairness of biometric identification based on gaze patterns. We find that while oculomotoric identification does not favor any particular gender and does not significantly favor by age range, it is unfair with respect to ethnicity. Moreover, fairness concerning ethnicity cannot be achieved by balancing the training data for the best-performing model.

Patterns of micro- and macro-movements of the eyes are highly individual and can serve as a biometric characteristic. It is also known that both alcohol inebriation and fatigue can reduce saccadic velocity and accuracy. This prompts the question of whether changes of gaze patterns caused by alcohol consumption and fatigue impact the accuracy of oculomotoric biometric identification. We collect an eye tracking data set from 66 participants in sober, fatigued and alcohol-intoxicated states. We find that after enrollment in a rested and sober state, identity verification based on a deep neural embedding of gaze sequences is significantly less accurate when probe sequences are taken in either an inebriated or a fatigued state. Moreover, we find that fatigue and intoxication appear to randomize gaze patterns: when the model is fine-tuned for invariance with respect to inebriation and fatigue, and even when it is trained exclusively on inebriated training person, the model still performs significantly better for sober than for sleep-deprived or intoxicated subjects.

We study involuntary micro-movements of both eyes, in addition to saccadic macro-movements, as biometric characteristic. We develop a deep convolutional neural network that processes binocular oculomotoric signals and identifies the viewer. In order to be able to detect presentation attacks, we develop a model in which the movements are a response to a controlled stimulus. The model detects replay attacks by processing both the controlled but randomized stimulus and the ocular response to this stimulus. We acquire eye movement data from 150 participants, with 4 sessions per participant. We observe that the model detects replay attacks reliably; compared to prior work, the model attains substantially lower error rates.

We study the problem of identifying viewers of arbitrary images based on their eye gaze. Psychological research has derived generative stochastic models of eye movements. In order to exploit this background knowledge within a discriminatively trained classification model, we derive Fisher kernels from different generative models of eye gaze. Experimentally, we find that the performance of the classifier strongly depends on the underlying generative model. Using an SVM with Fisher kernel improves the classification performance over the underlying generative model.

Distributional properties of fixations and saccades are known to constitute biometric characteristics. Additionally, high-frequency micro-movements of the eyes have recently been found to constitute biometric characteristics that allow for faster and more robust biometric identification than just macro-movements. Micro-movements of the eyes occur on scales that are very close to the precision of currently available eye trackers. This study therefore characterizes the relationship between the temporal and spatial resolution of eye tracking recordings on one hand and the performance of a biometric identification method that processes micro-and macro-movements via a deep convolutional network. We find that that the deteriorating effects of decreasing both, the temporal and spatial resolution are not cumulative. We observe that on low-resolution data, the network reaches performance levels above chance and outperforms statistical approaches.