用惯性传感器提升脑电运动伪影去除效果，让脑机接口更稳定。
IMU-Enhanced EEG Motion Artifact Removal with Fine-Tuned Large Brain Models
- 用大脑模型结合惯性数据，通过相关性注意力识别脑电信号中的运动伪影。
- 仅用5.9小时数据微调模型，在多种运动场景下显著优于传统方法。
- 适合需要高鲁棒性的脑机接口应用，尤其在动态环境中使用。
脑电图（EEG）虽具有高时间分辨率，但常受生理与环境伪影干扰，导致信噪比低。运动相关的脑电信号伪影是阻碍脑机接口实际应用的主要挑战之一。以往研究多依赖单一模态方法（如ASR、ICA），未融合同步采集的惯性测量单元（IMU）数据，而IMU可直接捕捉运动幅度与动态。本文提出基于微调大腦模型（LaBraM）的相关性注意力映射方法，利用IMU数据的空间通道关系定位脑电信号中的运动伪影。该微调模型约含920万参数，训练使用5.9小时脑电与惯性数据，仅为原始基线模型2500小时训练时长的0.2346%。在不同时间尺度和运动活动下，与经典ASR-ICA基准对比显示，引入IMU参考信号显著提升了模型在多样运动场景下的鲁棒性。
原文摘要 · Abstract (English)
Electroencephalography (EEG) is a non-invasive method for measuring brain activity with high temporal resolution; however, EEG signals often exhibit low signal-to-noise ratios because of contamination from physiological and environmental artifacts. One of the major challenges hindering the real-world deployment of brain-computer interfaces (BCIs) involves the frequent occurrence of motion-related EEG artifacts. Most prior studies on EEG motion artifact removal rely on single-modality approaches, such as Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA), without incorporating simultaneously recorded modalities like inertial measurement units (IMUs), which directly capture the extent and dynamics of motion. This work proposes a fine-tuned large brain model (LaBraM)-based correlation attention mapping method that leverages spatial channel relationships in IMU data to identify motion-related artifacts in EEG signals. The fine-tuned model contains approximately 9.2 million parameters and uses 5.9 hours of EEG and IMU recordings for training, just 0.2346\% of the 2500 hours used to train the base model. We compare our results against the established ASR-ICA benchmark across varying time scales and motion activities, showing that incorporating IMU reference signals significantly improves robustness under diverse motion scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。