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Multimodal data channels such as bio-physiological signals are increasingly used in game-play studies to better understand players' behaviours and their motivations.
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Repeated measurements and multimodal data are common in neuroimaging research.
A novel in-house integrated hardware and software experimentation platform, LabNeuro, was used to integrate these multimodal data.
The multimodal data used in the present study can be accessed upon request to the corresponding authors.
However, collocation of multimodal data across a consortium often leads to redundancies that complicate the iterative quality control processes inherent in research data analysis.
Memarian, N., Kim, S., Dewar, S., Engel, J. & Staba, R. J. Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.
Registration of multimodal data also creates a need for different display techniques and user-friendly interfaces.
In this paper we aim for improving the reliability and precision of localization of our multimodal data fusion algorithm.
Multimodal data fusion requires resolving several issues such as significantly different sampling frequencies of the individual modalities.
Omics data represent a massive source of multimodal data.
A multimodal data analysis approach was used (Kress 2009).
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