arXiv subjects

Zhongyu Tian

Publications and source records attributed to Zhongyu Tian.

3 recordsLinked to original sources

arXiv2026

PeakFlow: Peak-Guided Coarse-to-Refined Modeling for EEG-Based Dynamic Affective Trajectory Prediction

Most existing EEG-based emotion recognition studies formulate affective decoding as static category prediction, although emotions elicited by continuous stimulation evolve over time, accumulate, reach peak intensity, and then recover. This motivates EEG-based dynamic affective trajectory prediction, which estimates continuous affective intensity curves from sequential EEG observations. Existing temporal regression models can capture coarse intensity trends but often fail to preserve peak-centered structure, leading to inaccurate peak timing and terminal-peak bias, where the predicted maximum is shifted toward the end of a trial. To address this issue, we propose PeakFlow, a peak-guided coarse-to-refined framework for EEG-based dynamic affective trajectory prediction. PeakFlow first learns a coarse affective flow through EEG temporal tokenization and masked temporal modeling, then applies a lightweight residual refiner for peak-guided bounded calibration. The refiner uses trajectory-aware cues and a peak-centered objective combining global trajectory consistency, peak-zone emphasis, peak-probability localization, terminal suppression, and residual regularization. This design preserves the global affective trend while correcting peak misalignment, peak-value deviation, and false-terminal predictions. Leave-one-subject-out experiments on SEED-VII show that PeakFlow improves both global trajectory fitting and peak-centered temporal reliability over strong dynamic modeling baselines. Auxiliary evaluation on FIRMED further suggests its potential for sparse peak-centered ordinal intensity analysis. These results highlight the importance of peak-aware modeling for temporally faithful EEG-based dynamic emotion prediction. Code is available at https://github.com/jukebox333/PeakFlow.

cs.HC↗

arXiv2026

FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition

Traditional video-induced physiological datasets usually rely on whole-trial labels, which introduce temporal label noise in dynamic emotion recognition. We present FIRMED, a peak-centered multimodal dataset based on an immediate-recall annotation paradigm, with synchronized EEG, ECG, GSR, PPG, and facial recordings from 35 participants. FIRMED provides event-centered timestamps, emotion labels, and intensity annotations, and its annotation quality is supported by subjective and physiological validation. Benchmark experiments show that FIRMED consistently outperforms whole-trial labeling, yielding an average gain of 3.8 percentage points across eight EEG-based classifiers, with further improvements under multimodal fusion. FIRMED provides a practical benchmark for temporally localized supervision in multimodal affective computing.

cs.HC↗

arXiv2023

Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset

Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam, sourced from the Chinese National Medical Licensing Examination. CMExam consists of 60K+ multiple-choice questions for standardized and objective evaluations, as well as solution explanations for model reasoning evaluation in an open-ended manner. For in-depth analyses of LLMs, we invited medical professionals to label five additional question-wise annotations, including disease groups, clinical departments, medical disciplines, areas of competency, and question difficulty levels. Alongside the dataset, we further conducted thorough experiments with representative LLMs and QA algorithms on CMExam. The results show that GPT-4 had the best accuracy of 61.6% and a weighted F1 score of 0.617. These results highlight a great disparity when compared to human accuracy, which stood at 71.6%. For explanation tasks, while LLMs could generate relevant reasoning and demonstrate improved performance after finetuning, they fall short of a desired standard, indicating ample room for improvement. To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations. The experiments and findings of LLM evaluation also provide valuable insights into the challenges and potential solutions in developing Chinese medical QA systems and LLM evaluation pipelines. The dataset and relevant code are available at https://github.com/williamliujl/CMExam.

cs.CL↗