IMU-Enhanced EEG Motion Artifact Removal with Fine-Tuned Large Brain Models

• URL: http://arxiv.org/abs/2509.01073v1
• Date: Mon, 01 Sep 2025 02:29:14 GMT
• Title: IMU-Enhanced EEG Motion Artifact Removal with Fine-Tuned Large Brain Models
• Authors: Yuhong Zhang, Xusheng Zhu, Yuchen Xu, ChiaEn Lu, Hsinyu Shih, Gert Cauwenberghs, Tzyy-Ping Jung,
• Abstract summary: EEG signals often exhibit low signal-to-noise ratios because of contamination from physiological and environmental artifacts.<n>This work proposes a fine-tuned large brain model (LaBraM)-based correlation attention mapping method to identify motion-related artifacts in EEG signals.<n>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.
• Score: 6.611531256245718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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