AI 中文总结
研究基于脑电图的动态情感轨迹预测问题，提出PeakFlow框架，先通过脑电图时间标记化等学习粗略情感流，再用轻量级残差细化器校准，提升了全局轨迹拟合和峰值中心时间可靠性，突出峰值感知建模的重要性。
AI 中文摘要
大多数现有的基于脑电图的情感识别研究将情感解码视为静态类别预测，然而连续刺激引发的情感会随时间演变、积累、达到峰值强度然后恢复。这促使了基于脑电图的动态情感轨迹预测的产生，即从连续的脑电图观察中估计连续的情感强度曲线。现有的时间回归模型能捕捉粗略强度趋势，但常无法保留以峰值为中心的结构，导致峰值时间不准确和终端峰值偏差。为解决此问题，我们提出了PeakFlow，这是一个用于基于脑电图的动态情感轨迹预测的峰值引导粗到精框架。PeakFlow首先通过脑电图时间标记化和掩码时间建模学习粗略的情感流，然后应用轻量级残差细化器进行峰值引导的有界校准。细化器使用轨迹感知线索和一个以峰值为中心的目标，结合全局轨迹一致性、峰值区域强调、峰值概率定位、终端抑制和残差正则化。这种设计在纠正峰值错位、峰值偏差和错误终端预测的同时保留了全局情感趋势。在SEED-VII上的留一法实验表明，PeakFlow在强动态建模基线之上提高了全局轨迹拟合和以峰值为中心的时间可靠性。在FIRMED上的辅助评估进一步表明了其在稀疏的以峰值为中心的序数强度分析中的潜力。这些结果突出了峰值感知建模对于基于脑电图的时间忠实动态情感预测的重要性。代码可在该https网址获取。
英文摘要
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.
CommentsPreprint. Code is available at https://github.com/jukebox333/PeakFlow
