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Found 7 papers, 0 papers with code
Improving Multimodal Learning with Multi-Loss Gradient Modulation
Learning from multiple modalities, such as audio and video, offers opportunities for leveraging complementary information, enhancing robustness, and improving contextual understanding and performance.
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data.
Ranked #1 on
Sleep Stage Detection
on SHHS
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
In the course of fMRI, some sources of activation are caused by noise and artifacts.
Early soft and flexible fusion of EEG and fMRI via tensor decompositions
The fusion methods reported so far ignore the underlying multi-way nature of the data in at least one of the modalities and/or rely on very strong assumptions about the relation of the two datasets.
Assisted Dictionary Learning for fMRI Data Analysis
Extracting information from functional magnetic resonance (fMRI) images has been a major area of research for more than two decades.
Higher-Order Block Term Decomposition for Spatially Folded fMRI Data
The growing use of neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality.
