Electroencephalography (EEG) decoding requires models that can effectively extract and integrate complex temporal, spectral, and spatial features from multichannel signals. To address this challenge, we propose a lightweight and generalizable decoding framework named Hierarchical Convolutional Fusion Transformer (HCFT), which combines dual-branch convolutional encoders and hierarchical Transformer blocks for multi-scale EEG representation learning. Specifically, the model first captures local temporal and spatiotemporal dynamics through time-domain and time-space convolutional branches, and then aligns these features via a cross-attention mechanism that enables interaction between branches at each stage. Subsequently, a hierarchical Transformer fusion structure is employed to encode global dependencies across all feature stages, while a customized Dynamic Tanh normalization module is introduced to replace traditional Layer Normalization in order to enhance training stability and reduce redundancy. Extensive experiments are conducted on two representative benchmark datasets, BCI Competition IV-2b and CHB-MIT, covering both event-related cross-subject classification and continuous seizure prediction tasks. Results show that HCFT achieves 80.83% average accuracy and a Cohen's kappa of 0.6165 on BCI IV-2b, as well as 99.10% sensitivity, 0.0236 false positives per hour, and 98.82% specificity on CHB-MIT, consistently outperforming over ten state-of-the-art baseline methods. Ablation studies confirm that each core component of the proposed framework contributes significantly to the overall decoding performance, demonstrating HCFT's effectiveness in capturing EEG dynamics and its potential for real-world BCI applications.

翻译：脑电图（EEG）解码需要模型能够有效地从多通道信号中提取并整合复杂的时域、频域和空间特征。为应对这一挑战，我们提出了一种轻量级且可泛化的解码框架，称为层次化卷积融合Transformer（HCFT），该框架结合了双分支卷积编码器和层次化Transformer模块，用于多尺度脑电表征学习。具体而言，模型首先通过时域和时空卷积分支捕获局部时间动态与时空动态，随后通过跨注意力机制对齐这些特征，该机制实现了各阶段分支间的交互。之后，采用一种层次化Transformer融合结构来编码所有特征阶段间的全局依赖关系，同时引入一个定制的动态Tanh归一化模块以替代传统的层归一化，旨在增强训练稳定性并减少冗余。我们在两个代表性基准数据集BCI Competition IV-2b和CHB-MIT上进行了广泛实验，涵盖了事件相关的跨被试分类和连续癫痫发作预测任务。结果表明，HCFT在BCI IV-2b上取得了80.83%的平均准确率和0.6165的Cohen's kappa值，在CHB-MIT上获得了99.10%的灵敏度、每小时0.0236的误报率和98.82%的特异性，持续优于十余种最先进的基线方法。消融研究证实，所提出框架的每个核心组件均对整体解码性能有显著贡献，证明了HCFT在捕捉脑电动态方面的有效性及其在实际脑机接口应用中的潜力。