Linnéa Larsson

Detection of Saccades and Postsaccadic Oscillations in the Presence of Smooth Pursuit

A novel algorithm for detection of saccades and postsaccadic oscillations in the presence of smooth pursuit movements is proposed. The method combines saccade detection in the acceleration domain with specialized on- and offset criteria for saccades and postsaccadic oscillations. The performance of the algorithm is evaluated by comparing the detection results to those of an existing velocity-based adaptive algorithm and a manually annotated database. The results show that there is a good agreement between the events detected by the proposed algorithm and those in the annotated database with Cohens kappa around 0.8 for both a development and a test database. In conclusion, the proposed algorithm accurately detects saccades and postsaccadic oscillations as well as intervals of disturbances.

One algorithm to rule them all? : An evaluation and discussion of ten eye movement event-detection algorithms

Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9):2484–2493,2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

Detection of fixations and smooth pursuit movements in high-speed eye-tracking data

A novel algorithm for the detection of fixations and smooth pursuit movements in high-speed eye-tracking data is proposed, which uses a three-stage procedure to divide the intersaccadic intervals intoa sequence of fixation and smooth pursuit events. The first stage performs a preliminary segmentationwhile the latter two stages evaluate the characteristics of each such segment and reorganize the pre-liminary segments into fixations and smooth pursuit events. Five different performance measures arecalculated to investigate different aspects of the algorithm’s behavior. The algorithm is compared to thecurrent state-of-the-art (I-VDT and the algorithm in [11]), as well as to annotations by two experts. Theproposed algorithm performs considerably better (average Cohen’s kappa 0.42) than the I-VDT algorithm(average Cohen’s kappa 0.20) and the algorithm in [11] (average Cohen’s kappa 0.16), when comparedto the experts’ annotations. (Less)

Compensation of head movements in mobile eye-tracking data using an inertial measurement unit

Analysis of eye movements recorded with a mobile eye-tracker is difficult since the eye-tracking data are severely affected by simultaneous head and body movements. Automatic analysis methods developed for remote-, and tower-mounted eye-trackers do not take this into account and are therefore not suitable to use for data where also head- and body movements are present. As a result, data recorded with a mobile eye-tracker are often analyzed manually. In this work, we investigate how simultaneous recordings of eye- and head movements can be employed to isolate the motion of the eye in the eye-tracking data. We recorded eye-in-head movements with a mobile eye-tracker and head movements with an Inertial Measurement Unit (IMU). Preliminary results show that by compensating the eye-tracking data with the estimated head orientation, the standard deviation of the data during vestibular-ocular reflex (VOR) eye movements, was reduced from 8.0° to 0.9° in the vertical direction and from 12.9° to 0.6° in the horizontal direction. This suggests that a head compensation algorithm based on IMU data can be used to isolate the movements of the eye and therefore simplify the analysis of data recorded using a mobile eye-tracker.

Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation

An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate its performance using an automated video-based strategy. The proposed algorithm uses a clustering approach that takes both spatial and temporal aspects of the binocular eye-tracking signal into account, and is evaluated using a novel video-based evaluation strategy based on automatically detected moving objects in the video stimuli. The binocular algorithm detects 98% of fixations in image stimuli compared to 95% when only one eye is used, while for video stimuli, both the binocular and monocular algorithms detect around 40% of smooth pursuit movements. The present article shows that using binocular information for discrimination of fixations and smooth pursuit movements is advantageous in static stimuli, without impairing the algorithms ability to detect smooth pursuit movements in video and moving-dot stimuli. With an automated evaluation strategy, time-consuming manual annotations are avoided and a larger amount of data can be used in the evaluation process.

Discrimination of fixations and smooth pursuit movements in high-speed eye-tracking data

A novel three-stage algorithm for detection of fixations and smooth pursuit movements in high-speed eye-tracking data is proposed. In the first stage, a segmentation based on the directionality of the data is performed. In the second stage, four spatial features are computed from the data in each segment. Finally, data are classified into fixations and smooth pursuit movements based on a combination of the spatial features and the properties of neighboring segments. The algorithm is evaluated under the assumption that the intersaccadic intervals represent fixations in data recorded when viewing images, and mainly smooth pursuit movements in data recorded when viewing moving dots. The results show that the algorithm is able to detect 94.3% of the fixations for image stimuli, compared to a previous algorithm with 80.4% detected fixations. For moving dot stimuli the proposed algorithm detects 86.7% smooth pursuit movements compared to 68.0% for the previous algorithm.