用于脑电信号情绪识别的多尺度时间动态融合框架
A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition
AI总结：
本研究针对脑电情绪识别提出多尺度时间动态融合框架，在含混合情感类别的三分类任务中获45.43%准确率，优于基线及拼接方法，性能显著提升但计算量更大。
AI中文摘要：
混合情绪是自动情绪识别中具有临床相关性但仍未被充分探索的目标。脑电（EEG）可提供毫秒级的神经活动数据，但大多数脑电处理流程通过单一时间窗口分析信号，从而固定了模型可利用的时间结构。本研究提出一种用于脑电情绪识别的多尺度时间框架：将脑电波形分解为单个或多个时长的窗口，由共享的基于注意力的编码器处理，再通过动态融合模块整合，该模块为不同时间尺度分配样本特定权重。该框架在被试独立协议下的二分类和三分类场景中进行评估，其中三分类任务包含混合情感类别。二分类任务的最佳结果为65.22%，三分类任务为45.43%，均来自三尺度动态融合配置，且显著优于全信号基线。两个任务的最佳时间尺度存在差异；在得分最高的二分类配置中，动态融合性能优于拼接，在得分最高的三分类配置中略优于拼接，不过这些多尺度设置的计算量远高于全信号基线。
英文摘要：
Mixed emotions represent a clinically relevant but still underexplored target for automatic emotion recognition. EEG provides millisecond-level access to neural activity, yet most EEG pipelines analyze the signal through a single temporal window, thereby fixing the temporal structure available to the model. This study introduces a multi-scale temporal framework for EEG-based emotion recognition. The EEG waveform is decomposed into windows of one or several durations, processed by a shared attention-based encoder, and integrated through a dynamic fusion module that assigns sample-specific weights across temporal scales. The framework is evaluated under a subject-independent protocol in binary and three-class settings, with the three-class task including the mixed affective category. The best results are 65.22% for the two-class task and 45.43% for the three-class task. Both are obtained with three-scale dynamic-fusion configurations and remain substantially above the full-signal baseline. The best-performing temporal scales differ between the two tasks. Dynamic fusion outperforms concatenation in the highest-scoring two-class configuration and slightly exceeds it in the highest-scoring three-class configuration, although these multi-scale settings require substantially more computation than the full-signal baseline.
