IMU-integrated Artifact Subspace Reconstruction for Wearable EEG Devices

Electroencephalography (EEG) provides unique insights into natural brain dynamics outside the laboratory setting. However, its usability is limited due to the presence of artifacts. Artifact Subspace Reconstruction (ASR) has been a popular method for enhancing the signal-to-noise ratio (SNR) in mobile EEG; nonetheless, its complexity restricts its applicability on lightweight, resource-constrained EEG devices. To address this challenge, we propose an innovative IMU-integrated approach for artifacts correction (IMU-ASR). Specifically, we replace ASR's time-consuming calibration process with a simpler accelerometer-based method, significantly reducing computational time without compromising performance. We validate our approach on two publicly available datasets, one with low-density (8 channels) and the other with high-density (120 channels) EEG. Our findings demonstrate the potential of accelerometer-driven ASR for lightweight hardware-software EEG solutions, promising a more practical and efficient approach for artifact correction in mobile EEG applications.