Multimodal Latent Domain Learning for Neurological Disorder Prediction
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Solution Overview
Problem
Current technologies lack the ability to effectively integrate and analyze multiple modalities such as EEG, fMRI, and facial muscle movements to understand neurological disorders and cognitive states, due to the scarcity of labeled multimodal data.
Innovation Solution
The system employs source modality latent domain learning (SMDL) using neural networks to learn correlations between multiple modalities by applying signal transformations and training deep learning models with a small amount of labeled data, while aligning latent representations of different modalities using an alignment loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple modalities (EEG, fMRI, facial movements) are integrated to improve prediction accuracy of neurological disorders, then measurement precision and reliability improve, but device complexity and data processing requirements worsen
Solution Approach 1:
The system segments the complex multimodal data processing into separate modality-specific encoders (EEG encoder, fMRI encoder, facial movement encoder) that each process one type of data independently, then combines their outputs in a unified prediction framework. This segmentation reduces the complexity of handling all modalities simultaneously while maintaining the ability to capture cross-modality correlations.
Solution Approach 2:
The patent introduces latent domain representations as intermediary variables that mediate between the different modalities and the final prediction. These latent representations serve as a common language that allows the system to integrate information from EEG, fMRI, and facial movements without directly processing all raw data together, thereby reducing system complexity.
2Measurement precision
If deep learning models are trained with multiple modalities to improve cognitive state detection, then measurement precision improves, but loss of time for data collection and processing worsens
Solution Approach 1:
The system performs preliminary encoding of each modality's data into latent representations before the main prediction task. The modality-specific encoders pre-process EEG, fMRI, and facial movement data separately, extracting relevant features and reducing data dimensionality in advance. This preliminary action reduces the processing time required during actual prediction while maintaining detection accuracy.
3Reliability
If labeled multimodal data is used for training to improve prediction reliability, then reliability improves, but loss of substance (data requirements) worsens
Solution Approach 1:
The patent employs a unified prediction framework that serves multiple functions: it can process any combination of the three modalities (EEG, fMRI, facial movements) and adapt to different data availability scenarios. This multi-functional model can operate with complete multimodal data, partial modalities, or even single modality inputs, reducing the need for extensively labeled multimodal datasets while maintaining reliable predictions through transfer learning and domain adaptation.
Data Source
AI summary
The present disclosure presents system and methods for obtaining multimodal input data of a subject, wherein the multimodal input data comprises at least two input modalities of data; extracting features from the multimodal input data; learning multimodal signal correlations and source latent distribution alignment from the extracted features of the multimodal input data; training and optimizing a multimodal machine learning algorithm on input labeled data to learn local features of each modality of the multimodal input data; and/or executing, by a computer system, the trained multimodal machine learning algorithm to predict a cognitive state or disorder of the subject using the learned multimodal signal correlations and source latent distribution alignment.