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Multimodality and multichannel monitoring have become increasingly popularand accessible in engineering, Internet of Things, wearable devices, andbiomedical applications. In these contexts, given the diverse and complexnature of data modalities, the relevance of sensor fusion and sensor selectionis heightened. In this note, we study the problem of channel/modality selectionand fusion from an information theoretical perspective, focusing on linear andnonlinear signal mixtures corrupted by additive Gaussian noise. We revisit andextend well-known properties of linear noisy data models in estimation andinformation theory, providing practical insights that assist in thedecision-making process between channel (modality) selection and fusion. Usingthe notion of multichannel signal-to-noise ratio, we derive conditions underwhich, selection or fusion of multimodal/multichannel data can be beneficial orredundant. This contributes to a better understanding of how to optimize sensorfusion and selection from a theoretical standpoint, aiming to enhancemultimodal/multichannel system design, especially for biomedicalmultichannel/multimodal applications.

Notes on Information Propagation in Noisy Multichannel Data Models: Insights into Sensor Selection and Fusion in Multimodal Biomedical Applications