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.
Registration of multimodal data also creates a need for different display techniques and user-friendly interfaces.
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.
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.
Automatic scene understanding from multimodal data is a key task in the design of fully autonomous vehicles.
In addition, novel experimental designs that combine optical and electrophysiological methods will depend upon statistical tools that combine multimodal data.
In this paper, we study hamming metric learning in the context of multimodal data for cross-view similarity search.
