Algorithm for automatic analysis of electro-oculographic data

BioMedical Engineering OnLine

Kati Pettersson¹  Sharman Jagadeesan¹  Kristian Lukander¹  Andreas Henelius¹  Edward Hæggström²  Kiti Müller¹ 

[1] Brain Work Research Center, Finnish Institute of Occupational Health, Topeliuksenkatu 41aA, Helsinki 00250, Finland

[2] Electronics Research Laboratory, Department of Physics, University of Helsinki, Gustaf Hällströmin katu 2, P. O. Box 64, Helsinki FIN-00014, Finland

关键词: Data mining;    Auto-calibrating;    Blink;    Saccade;    EOG;   

Others  :  797293
DOI  :  10.1186/1475-925X-12-110

 received in 2013-05-21, accepted in 2013-10-18,  发布年份 2013

【 摘 要 】

Background

Large 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.

Methods

The 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.

Results

The 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.

Conclusion

The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

【 授权许可】

   
2013 Pettersson et al.; licensee BioMed Central Ltd.

【 图 表 】

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