ArticleFrontiers in neuroergonomics2024
Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA.
Article in Frontiers in neuroergonomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A portable solution for simultaneous human movement and mobile EEG acquisition: readiness potential for basketball free-throw shooting.Experimental brain research · 2026Article
- SSDLabeler: realistic semi-synthetic data generation for multi-label artifact classification in EEG.Scientific reports · 2026Article
- Suppressing Non-Stationary Motion Artefacts in Mobile EEG Using Generalized Eigenvalue Decomposition.Sensors (Basel, Switzerland) · 2026Article
- Fusion of Multi-Paradigm EEG Microstate Features to Enhance the Recognition of Mild Cognitive Impairment.Brain and behavior · 2026Article
- The Relation of Alpha Asymmetry to Physical Activity Duration and Intensity.Brain sciences · 2025Article
- A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running.Sensors (Basel, Switzerland) · 2025Article
- Neural Oscillatory Mechanisms Underlying Step Accuracy: Integrating Microstate Segmentation with eLORETA-Independent Component Analysis.Brain sciences · 2025Article
- Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.Scientific reports · 2025Article
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4 authors.
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Abstract
Introduction: To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp. Methods: A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment. Results: Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects. Discussion: This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.
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