10.6084/m9.figshare.1288914.v2 Seonjoo Lee Seonjoo Lee Brian S. Caffo Brian S. Caffo Balaji Lakshmanan Balaji Lakshmanan Dzung L. Pham Dzung L. Pham Evaluating model misspecification in independent component analysis Taylor & Francis Group 2015 convolutive ICA algorithms source separation technique component analysis Independent component analysis Current ICA approaches ICA algorithms convolutive ICA algorithm Simulation studies show model 2015-01-13 16:43:36 Journal contribution https://tandf.figshare.com/articles/journal_contribution/Evaluating_model_misspecification_in_independent_component_analysis/1288914 <div><p>Independent component analysis (ICA) is a popular blind source separation technique used in many scientific disciplines. Current ICA approaches have focused on developing efficient algorithms under specific ICA models, such as instantaneous or convolutive mixing conditions, intrinsically assuming temporal independence or autocorrelation of the sources. In practice, the true model is not known and different ICA algorithms can produce very different results. Although it is critical to choose an ICA model, there has not been enough research done on evaluating mixing models and assumptions, and how the associated algorithms may perform under different scenarios. In this paper, we investigate the performance of multiple ICA algorithms under various mixing conditions. We also propose a convolutive ICA algorithm for echoic mixing cases. Our simulation studies show that the performance of ICA algorithms is highly dependent on mixing conditions and temporal independence of the sources. Most instantaneous ICA algorithms fail to separate autocorrelated sources, while convolutive ICA algorithms depend highly on the model specification and approximation accuracy of unmixing filters.</p></div>