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Hypoxia State Prediction Model Based on Mirror Feature Curve (MFC) and Attractor Algorithm
Developing a novel predictive framework that leverages mirror feature curves extracted from physiological signals combined with attractor-based dynamical systems analysis to forecast hypoxic episodes in high-altitude environments.
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Hypoxia State Prediction Based on MFC and Manifold Convolution Algorithm
Integrating mirror feature curve representations with manifold-aware convolutional neural networks to capture the intrinsic geometric structure of physiological data for improved hypoxia classification and prediction.
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Hypoxia Event Modeling and Prediction Based on Graph Neural Networks
Constructing graph-based representations of multi-channel physiological signals to model inter-channel dependencies and temporal dynamics for comprehensive hypoxia event detection and forecasting.
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All-Optical Closed-Loop Brain-Computer Interface Based on TCN and Attractor Algorithm
Designing a fully optical brain-computer interface system utilizing temporal convolutional networks and attractor dynamics for real-time neural signal decoding and closed-loop neuromodulation.
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Dimensional Game Guide: Low-Dimensional Dynamics Modeling for Brain-Computer Interfaces
Exploring low-dimensional manifold representations of neural population activity to develop interpretable and efficient decoding algorithms for next-generation brain-computer interfaces.
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Physics-Informed Neural Decoding: Synthetic-to-Real Domain Transfer
Leveraging physics-informed neural networks to bridge the gap between synthetic training data and real neural recordings, enabling robust cross-domain generalization in neural decoding tasks.