Synchronization of acquisition devices in neuroimaging: An application using co-registration of eye movements and electroencephalography
Résumé
Interest in applications for the simultaneous acquisition of data from different devices is growing. In neuroscience for
example, co-registration complements and overcomes some of the shortcomings of individual methods. However, precise
synchronization of the different data streams involved is required before joint data analysis. Our article presents and evaluates
a synchronization method which maximizes the alignment of information across time. Synchronization through common
triggers is widely used in all existing methods, because it is very simple and effective. However, this solution has been found
to fail in certain practical situations, namely for the spurious detection of triggers and/or when the timestamps of triggers
sampled by each acquisition device are not jointly distributed linearly for the entire duration of an experiment. We propose
two additional mechanisms, the "Longest Common Subsequence" algorithm and a piecewise linear regression, in order to
overcome the limitations of the classical method of synchronizing common triggers. The proposed synchronization method
was evaluated using both real and artificial data. Co-registrations of electroencephalographic signals (EEG) and eye movements
were used for real data. We compared the effectiveness of our method to another open source method implemented
using EYE-EEG toolbox.
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