用对比学习提升脑电解码，多尺度卷积+Transformer更高效
CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding
- 采用多尺度卷积与对比学习预训练，适配脑电信号噪声大、频带窄特点
- 在多种电极配置下表现优于现有重建预训练模型，且无需预训练即达先进水平
- 适合脑机接口、神经疾病研究等需高效解码脑电的场景
自监督预训练基础模型在非侵入式脑电（EEG）解码中展现出早期潜力。近年来多数大规模模型采用对原始脑电信号分块并进行掩码重建预训练。然而，这种策略在脑电这类噪声高、信息集中于有限频带的数据上表现不佳。基于此，我们提出一种新型对比学习预训练脑电模型——多尺度卷积变换器（CoCoT），其包含多尺度时间卷积输入层与Transformer编码器模块。CoCoT在多种异构电极配置的基准解码任务中表现匹配或超越当前最优的重建预训练模型。此外，从零开始训练的CoCoT性能优于以往单任务解码模型，甚至媲美预训练模型，展现出架构的灵活性与数据效率。通过系统消融实验，包括模型结构与预训练目标，验证了对比学习构建脑电基础模型的可行性，并提出了关键设计建议，推动对替代性大规模预训练策略的进一步探索。
原文摘要 · Abstract (English)
Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications. Many recent large-scale models converged on the approach of tokenizing raw EEG followed by masked reconstruction pretraining. However, this recipe has been shown to be suboptimal for data, like EEG, with high noise amplitude and information confined to limited dimensions such as narrow frequency bands. Building on this insight, we develop a novel contrastive-pretrained EEG model with multiscale temporal convolution input layers and Transformer encoder blocks (CoCoT). CoCoT matches or beats state-of-the-art reconstruction-pretrained EEG models on extensive benchmark decoding tasks with heterogeneous electrode configurations. Furthermore, CoCoT trained from scratch outperforms previous single-task decoding models and even rivals pretrained models, showcasing the architecture's flexibility and data efficiency. Through systematic ablations, including model architecture and pretraining objective, we demonstrate the viability of contrastive learning for building EEG FMs while suggesting key architectural design considerations, prompting further investigations in alternative large-scale pretraining strategies.
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