EEG-Text Anomaly Detection with PU Contrastive Learning
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Solution Overview
Problem
Existing medical diagnostic methods face challenges with limited detection accuracy due to imbalanced multimodal data, high labeling costs, and difficulty in obtaining labeled multimodal data, leading to inefficient and inaccurate anomaly detection.
Innovation Solution
An anomaly detection method using PU contrastive learning within a multimodal prototype network, incorporating a dilated convolutional network, BERT model, and multi-head self-attention mechanism for feature extraction and fusion, combined with a self-supervised contrast learning strategy to estimate unbiased risk and correct data imbalance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data enhancement techniques are used to balance positive and negative samples, then the number of positive samples is increased, but the intrinsic correlation of data is not fully captured and additional noise is introduced
Solution Approach 1:
The system uses self-supervised learning where the model learns from unlabeled multimodal data itself without requiring external labeling. The contrastive learning mechanism enables the model to automatically discover patterns and correlations in the data, making the system self-sufficient in learning from the available data distribution.
Solution Approach 2:
The patent changes the learning paradigm from supervised classification to self-supervised contrastive learning, fundamentally altering how the model processes data. By using contrastive loss functions and information bottleneck principles, the system transforms the learning objective to focus on capturing intrinsic data correlations without relying on labeled examples.
2Loss of information
If semi-supervised learning is used to learn from unlabeled data, then useful information is extracted from unlabeled samples, but model convergence is difficult and collapse is easy
Solution Approach 1:
The system performs preliminary feature extraction and representation learning from unlabeled multimodal data using contrastive learning before final classification. By pre-learning robust feature representations that capture the intrinsic structure of the data, the model is better prepared for subsequent supervised fine-tuning, improving convergence stability.
Solution Approach 2:
The patent implements a feedback mechanism where the model's predictions on unlabeled data are used to generate pseudo-labels, which then feed back into the training process. This iterative refinement allows the model to progressively improve its understanding of the data distribution while maintaining stable convergence through controlled feedback loops.
3Measurement precision
If labeled multimodal data is obtained for training, then detection accuracy is improved, but labeling cost is high
Solution Approach 1:
The system uses only a small subset of labeled positive samples combined with a large amount of unlabeled data for training. By applying self-supervised learning techniques, the model achieves effective learning without requiring extensive labeled data, significantly reducing labeling costs while maintaining high detection accuracy.
Solution Approach 2:
The patent introduces self-supervised contrastive learning as an intermediary mechanism that bridges the gap between unlabeled and labeled data. This intermediary learning process enables the model to learn meaningful representations from unlabeled data that can be effectively transferred to the final classification task, reducing dependency on expensive labeled data.
4Loss of time
If single modal detection is used, then labeling cost is reduced, but detection accuracy is limited
Solution Approach 1:
The system merges multiple modalities (EEG signals and text data) into a unified multimodal representation framework. By combining complementary information from different data sources through feature fusion and joint learning, the system achieves superior detection accuracy that exceeds what any single modality could provide alone.
Solution Approach 2:
The patent creates a universal multimodal detection framework that can process and integrate different types of data (physiological signals, text, and potentially other modalities). This multi-functional system is designed to handle diverse input types simultaneously, making it adaptable to various diagnostic scenarios while maintaining high accuracy.
Data Source
AI summary
An anomaly detection method based on PU contrastive learning within a multimodal prototype network that employs dilated convolutional networks and Bert models to form a multimodal data (EEG and text) feature extraction and fusion network. Through a multimodal feature enhanced prototype network, clustering is performed, but the results are biased due to the lack of labeled negative samples. Finally, a positive unlabeled learning method that integrates contrastive learning is used to estimate the unbiased risk of the biased clustering results, correct the deviation, and accurately identify the positive and negative samples. By analyzing a limited number of multimodal positive samples and a large amount of unlabeled data, the anomaly detection method can accurately classify positive samples and negative samples without the need for expensive manual labeling costs. It also adopts a self-supervised learning framework, integrating PU learning into contrastive learning to correct the classification deviation.