对比五种位置编码策略，发现任务不同最优方案也不同。
Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models
- 在CBraMod框架中测试五种位置编码方法
- 运动想象任务用球面编码效果最好，情绪识别则用非对称条件编码更优
- 提示位置编码需根据具体任务选择，无通用解
脑电图（EEG）是脑机接口中广泛使用的无创脑活动测量技术。传统监督式解码模型在跨任务、跨被试和跨数据集时泛化能力差，促使基于自监督学习的Transformer型脑电基础模型发展。由于Transformer具有置换不变性，需显式引入位置信息。与文本词元不同，EEG电极分布在头皮上，如何编码电极位置成为关键问题。本研究在CBraMod架构中评估五种位置编码策略，在运动想象分类与情绪识别任务上采用线性探测与微调协议进行测试。结果表明：无单一策略在所有任务中表现最佳。球面位置编码（SPE）在运动想象任务中表现优异，但在情绪识别中表现较差；而非对称条件位置编码（ACPE）在多任务间表现更一致。研究揭示最优位置编码策略依赖任务特性，不存在适用于所有脑电解码场景的通用方案。
原文摘要 · Abstract (English)
Electroencephalography (EEG) is a widely used non-invasive technique for measuring brain activity in brain-computer interface (BCI) applications. Supervised EEG decoding models often struggle to generalize across tasks, subjects, and datasets, motivating transformer-based EEG foundation models trained with self-supervised learning. Since transformers are permutation-invariant, they require explicit positional information. Unlike textual tokens, EEG electrodes are spatially distributed across the scalp, raising the question of how electrode positions should be encoded in transformer-based EEG models. In this study, we benchmark five positional encoding strategies within the CBraMod backbone and evaluate them under linear probing and fine-tuning protocols on motor imagery classification and emotion recognition. Our results show that no single strategy consistently outperforms across tasks. Spherical Positional Encoding (SPE) yields strong representations for motor imagery but underperforms on emotion recognition, while Asymmetric Conditional Positional Encoding (ACPE) demonstrates more consistent performance across tasks. These findings suggest that the optimal positional encoding strategy is task-dependent, with no universal solution across EEG decoding scenarios.
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