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LLM自动化标注情绪轨迹fMRI解码方法论。使用多目标回归框架、动态功能连接(DFC)、图论可解释AI解码连续情绪维度。
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Graph-native Python reimplementation of the Information Dynamics of Music (IDyOM) model that represents predictive memories as explicit graph objects for musical expectation modeling and network analysis.
Physics-aware end-to-end deep reinforcement learning methodology for quadcopter control with actuator dynamics modeling.
Reinforced Dreamer methodology for asymmetric reinforcement learning using latent guidance to improve world model representations and behaviors in model-based RL.
Based on SOC occupation classification
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| name | llm-emotion-trajectory-fmri |
| description | LLM自动化标注情绪轨迹fMRI解码方法论。使用多目标回归框架、动态功能连接(DFC)、图论可解释AI解码连续情绪维度。 |
| platforms | ["linux","macos","windows"] |
| tags | ["emotion","llm-annotation","dfc","fmri-decoding","regression","xai"] |
标题: Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework arXiv: 2606.07707v1 作者: Lemei Zhang, Peng Liu等 发布: 2026-06-05
关键词: llm-emotion, dfc-fmri, sentiment-trajectory, naturalistic-decoding, graph-xai, regression-framework
