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