Abstract
We propose an alternative method to align the polarities of independent components (ICs) for group-level IC cluster analysis. Current methods are presently limited in how indeterminacy of IC polarities is handled, as when multiplying a weight matrix to a time-series IC activation, the result from 1 × 1 and − 1 × − 1 are indistinguishable. We first clarify the EEGLAB’s default solution and define it as the iterative correlation maximization as it maximizes the within-cluster correlations of the IC scalp topographies to the cluster mean. We then propose the covariance maximization method, which determines the polarity of ICs based on the sign of the largest eigenvalue of covariance matrix. We compared the two methods on datasets from a published visual event-related potential (ERP) study. The results were similar when both methods were applied to the IC scalp topographies. However, when the proposed method was applied to IC ERPs, the number of clusters that showed significant ERP amplitudes increased from 5 to 9 out of 9 due to minimization of within-cluster ERP amplitude cancellation. Our study confirm covariance maximization provides an alternative solution to post-ICA group-level analysis that can maximize sensitivity of IC ERPs.
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The datasets analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
MM is supported by NSF 2011716 CRCNS US-Japan Research Proposal: A computational neuroscience approach to skill acquisition and transfer from visuo-haptic VR to the real-world and NINDS 5R01NS047293-16 ‘EEGLAB: Software for Analysis of Human Brain Dynamics’. MN and MM are supported by The Swartz Foundation (Old Field, New York). The authors express gratitude to Dr. Yash B. Joshi for encouraging the current study and revising the manuscript.
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M.N. and M.M. wrote the main text. M.M. conducted data analyses and generated figures. All authors reviewed the manuscript.
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Nakanishi, M., Miyakoshi, M. Revisiting Polarity Indeterminacy of ICA-Decomposed ERPs and Scalp Topographies. Brain Topogr 36, 223–229 (2023). https://doi.org/10.1007/s10548-023-00944-1
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DOI: https://doi.org/10.1007/s10548-023-00944-1