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Wearable low-density dry electroencephalogram (EEG) headsets facilitate multidisciplinary applications of brain-activity decoding and brain-triggered interaction for healthy people in real-world scenarios. However, movement artifacts pose a great challenge to their validity in users with naturalistic behaviors (i.e., without highly controlled settings in a laboratory). High-precision, high-density EEG instruments commonly embed an active electrode infrastructure and/or incorporate an auxiliary artifact subspace reconstruction (ASR) pipeline to handle movement artifact interferences. Existing endeavors motivate this study to explore the efficacy of both hardware and software solutions in low-density and dry EEG recordings against non-tethered settings, which are rarely found in the literature. Therefore, this study employed a LEGO-like electrode-holder assembly grid to coordinate three heterogeneous 3-channel system designs (with passive/active dry vs. passive wet electrodes). It also conducted a simultaneous EEG recording while performing an oddball task during treadmill walking, with speeds of 1 and 2 KPH. The quantitative metrics of pre-stimulus noise, signal-to-noise ratio, and inter-subject correlation (ISC) from the collected event-related potentials of 18 subjects were assessed. Results indicate that while treating a passive-wet system as benchmark, only the active-electrode design more or less rectified movement artifacts for dry electrodes, whereas the ASR pipeline was substantially compromised by limited electrodes. These findings suggest that a lightweight, minimally obtrusive dry EEG headset should at least equip an active-electrode infrastructure to withstand realistic movement artifacts for potentially sustaining its validity and applicability in real-world scenarios.

Movement Artifact Suppression in Wearable Low-Density and Dry EEG Recordings Using Active Electrodes and Artifact Subspace Reconstruction