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About this paper
Noga Mudrik, Yuxi Chen, Gal Mishne, Adam S. Charles
MILCCI analyzes labeled, multi-trial time series by sharing sparse components across trials while allowing label-specific channel adjustments and trial-specific activity traces. In a synthetic test with four components, 250 trials, and 500 time points per trial, it achieved the highest similarity to known structure among methods using the same dimension. This supports synthetic recovery, not causal interpretation of real-data associations.
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