比较五种模型在自然脑电解码中的表现，发现S5高效准确，EEGXF更稳健。
Temporal Context and Architecture: A Benchmark for Naturalistic EEG Decoding
- 对比CNN、LSTM、EEGXF、S4、S5五种架构在不同时间上下文下的表现。
- 64秒上下文下S5达98.7%准确率，参数量仅为CNN的1/20。
- 适合追求高精度或强鲁棒性的研究者，尤其关注模型可靠性时选EEGXF。
我们研究了模型架构与时间上下文在自然脑电解码中的相互作用。基于HBN电影观看数据集，对五种架构（CNN、LSTM、稳定化Transformer EEGXF、S4、S5）在8秒至128秒片段长度上进行4分类任务的基准测试。随着上下文长度增加，准确率提升：在64秒时，S5达到98.7%±0.6，CNN为98.3%±0.3，而S5参数量仅约为CNN的1/20。为评估真实场景鲁棒性，进一步测试零样本跨频率偏移、跨任务分布外输入及留一被试泛化能力。S5在跨被试任务中表现更强，但对分布外任务过于自信；EEGXF在频率偏移下更保守稳定，但在分布内校准性较差。结果揭示实际效率-鲁棒性权衡：S5适用于参数高效极致精度，EEGXF适用于需鲁棒性和保守不确定性估计的场景。
原文摘要 · Abstract (English)
We study how model architecture and temporal context interact in naturalistic EEG decoding. Using the HBN movie-watching dataset, we benchmark five architectures, CNN, LSTM, a stabilized Transformer (EEGXF), S4, and S5, on a 4-class task across segment lengths from 8s to 128s. Accuracy improves with longer context: at 64s, S5 reaches 98.7%+/-0.6 and CNN 98.3%+/-0.3, while S5 uses ~20x fewer parameters than CNN. To probe real-world robustness, we evaluate zero-shot cross-frequency shifts, cross-task OOD inputs, and leave-one-subject-out generalization. S5 achieves stronger cross-subject accuracy but makes over-confident errors on OOD tasks; EEGXF is more conservative and stable under frequency shifts, though less calibrated in-distribution. These results reveal a practical efficiency-robustness trade-off: S5 for parameter-efficient peak accuracy; EEGXF when robustness and conservative uncertainty are critical.
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