Abstract
BackgroundLarge amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets.MethodsThe auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks.ResultsThe algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate.ConclusionThe developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.
Highlights
Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized
Experiment 1 We analyzed 1920 saccades, of which 231 saccades were removed from the final analysis because of clear artifacts from subject movement
For vertical saccades with an amplitude of 7.5 or 10 deg, the detection sensitivity was higher than 94%
Summary
Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. Electro-oculography (EOG) is routinely registered by electroencephalography (EEG) setups to allow removal of eye movement artifacts [6] Parameters derived from both EEG [7,8,9,10] and EOG [11,12,13,14,15] have shown to be promising indicators of fatigue. Large amounts of EOG data is underutilized, since the traditional EOG metrics require calibrating the relationship between recorded voltage and the corresponding eye movement. To address this issue we set out to develop a robust, automatic, auto-calibrating algorithm that classifies temporal eye parameters (saccades, blinks) from EOG data
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