Unsupervised clustering method to detect microsaccades

TitleUnsupervised clustering method to detect microsaccades
Publication TypeConference Paper
Year of Publication2013
AuthorsOtero-Millán, J, Castro, JLAlba, Macknik, SL, Martínez-Conde, S
Conference NameNeuroscience 2013
Date Published11/2013
PublisherSociety for Neuroscience
Conference LocationSan Diego, CA, USA
Keywordsclustering, eye tracking, microsaccade detection
AbstractMicrosaccades, small involuntary eye movements that occur once or twice per second during attempted visual fixation, are relevant to perception, cognition, and oculomotor control, and present distinctive characteristics in visual and oculomotor pathologies. Thus, the development of robust and accurate microsaccade detection techniques is important for basic and clinical neuroscience research. Due to the diminutive size of microsaccades, automatic and reliable detection can be difficult, however. Current challenges in microsaccade detection include reliance on set arbitrary thresholds and lack of objective validation. Here we describe a novel microsaccade detecting method, based on unsupervised clustering techniques, that does not require an arbitrary threshold and provides a detection reliability index. We validated the new clustering method using real and simulated eye movement data. The clustering method reduced detection errors by 62% for binocular data and 72% for monocular data, when compared to standard contemporary microsaccade detection techniques. Further, the clustering method’s reliability index was correlated with the microsaccade detection error rate, suggesting that the reliability index may be used to evaluate the precision of eye tracking devices.
Citation Key529