Mining the Independent Source of ERP Components with ICA Decomposition

Independent component analysis (ICA) can blindly separates the input ERP data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain regions. In this study, we use ICA to illustrate that the P300 components in two ERPs recorded under various conditions or tasks are both mainly contributed from a few independent sources. ICA decomposition also indicates a new method to compare P300 components between two ERPs induced by two related tasks. Our comparisons are made on those independent sources contributed to the P300 components, rather than on the ERP waveforms directly. This novel approach identifies not only the similar or common independent components in both conditions that bring about a common part in ERP time courses, but also those different components induced by the different parts in ERP waveforms. Our study suggests that the ICA method is a useful tool to study the brain dynamics.

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