ScaleBrain：仅 500 万参数的 EEG 基础模型预训练框架
ScaleBrain: A Scale-Consistent Framework for EEG Foundation Models
做脑机接口或 EEG 表征学习的朋友可以看看这篇论文。它把多尺度建模做到了输入和预训练目标两端，6 个 BCI 基准里 5 个拿了最好的 Balanced Accuracy，参数才 5.0M，比 12 个基线都省。写论文选基线时也用得上。
论文提出 ScaleBrain，一个尺度一致性的 EEG 基础模型预训练框架，在六个公开 BCI 基准上与 12 个代表性基线对比，在五个数据集上取得最高平均 Balanced Accuracy，其余一个排名第三，而参数量仅 5.0M。
技术上，ScaleBrain 从输入和优化目标两端协调多尺度表征：引入多尺度 EEG tokenization，用不同时间跨度、共享时间锚点的分析窗口表示同一段 EEG 信号，并配合尺度特定的重建任务，使自监督学习在各时间分辨率上都获得充分监督。作者还设计了轴向时空注意力机制，对融合后的多尺度表征在空间和时间维度上建模上下文。
现有 EEG 基础模型多在特征层面构建时间尺度，尺度与原始信号特性的对应关系不明确，部分尺度的监督可能不足。ScaleBrain 针对这一问题显式对齐多尺度输入与自监督目标，对脑机接口方向的研究者具有直接参考价值。
ScaleBrain: A Scale-Consistent Framework for EEG Foundation Models
Foundation models pre-trained on large unlabeled electroencephalography (EEG) corpora have emerged as a transformative paradigm for Brain-Computer Interface (BCI) applications. Recent EEG foundation models have shown that multi-scale representations derived from fixed-length patches can improve EEG representation learning. Existing frameworks commonly construct temporal scales through feature-level operations. However, these operations leave the correspondence between learned features and scale-specific characteristics of the original EEG signals implicit. Moreover, when the self-supervised objective is not aligned with the multi-scale input representations, information at some temporal scales may receive insufficient supervision during pre-training. In light of this, we propose ScaleBrain, a scale-consistent pre-training framework that explicitly coordinates multi-scale representations of EEG from both the model input and optimization objective sides. Specifically, ScaleBrain introduces multi-scale EEG tokenization to represent the same EEG segment through analysis windows with different temporal spans and shared temporal anchors, and pairs these representations with scale-specific reconstruction to align with the multi-scale input structure and provide supervision across temporal resolutions during self-supervised learning. We further develop an axial spatio-temporal attention mechanism to contextualize the fused multi-scale representation along spatial and temporal dimensions. Extensive evaluations on six public BCI benchmarks against 12 representative baselines show that ScaleBrain achieves the highest mean Balanced Accuracy on five datasets and ranks third on the remaining one, with only 5.0M parameters, demonstrating strong performance and favorable parameter efficiency.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include ScaleBrain, EEG, 脑机接口, 自监督学习.
Source attribution: curated from arXiv cs.LG (@Minyu Zhou, Yajing Wu, Yongqiang Tang, Wang Jia, Wensheng Zhang).