Gait-Induced Myoelectric EEG Artifact Removal Validation from Conventional and Tripolar Concentric Ring Electrodes
Source abstract
Abstract
(1) Background: Understanding neural dynamics during human movement is a core neuroscience objective, yet there are fundamental challenges to the collection of high-fidelity neuroelectric signals during motion. We investigated the effects of electroencephalography (EEG) electrode design for cleaning high-density EEG, using an electrical testbed that mimicked the human head. (2) Methods: We used a 60-channel high-density array of tripolar concentric ring electrodes and conventional disk electrodes to compare the recovery of simulated brainwave activity in the presence of electrical neck muscle artifacts during walking. Simulated brainwave activity consisted of randomly occurring sinusoidal bursts with unique frequency content within human EEG spectral bands (5–37 Hz). Electrical neck muscle activity was recorded from a human subject during walking and broadcast into the head phantom device at scaled surface recording amplitudes (0× 0.5× 0.67×, 1×, 1.5×, 2×). We compared the number and spatial distribution of detected neural sources among electrode channels based on spectral power. (3) Results: At low muscle activation amplitudes, conventional electrodes identified more spectral power peaks (p ≤ 0.01) among more electrodes (p < 0.05) compared to tripolar concentric ring electrodes, indicating poorer spatial selectivity. At greater muscle artifact amplitudes, conventional electrodes identified fewer neural spectral power peaks (p < 0.05) with lesser localization accuracy (p < 0.05) compared to tripolar concentric ring electrodes. (4) Conclusions: We identified improved myoelectric artifact removal from tripolar concentric ring electrode recordings compared to conventional electrodes, offering a promising approach for recovering high-fidelity electrocortical activity from human subjects during locomotion.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA
Funding
Texas A&M Innovation[X] program
References (57)
Show source-provided references
1. Noninvasive Mobile EEG as a Tool for Seizure Monitoring and Management: A Systematic Review10.1111/epi.17220 ↗
2. Mobile EEG in Research on Neurodevelopmental Disorders: Opportunities and Challenges10.1016/j.dcn.2019.100635 ↗
3. Buskila, Y., Bellot-Saez, A., and Morley, J.W. (2019). Generating Brain Waves, the Power of Astrocytes. Front. Neurosci., 13.10.3389/fnins.2019.01125 ↗
4. Comparison of Signal Processing Methods for Reducing Motion Artifacts in High-Density Electromyography During Human Locomotion10.1109/OJEMB.2020.2999782 ↗
5. EEG Artifact Removal—State-of-the-Art and Guidelines10.1088/1741-2560/12/3/031001 ↗
6. Electrocortical Activity Is Coupled to Gait Cycle Phase during Treadmill Walking10.1016/j.neuroimage.2010.08.066 ↗
7. Song, S., and Nordin, A.D. (2021). Mobile Electroencephalography for Studying Neural Control of Human Locomotion. Front. Hum. Neurosci., 15.10.3389/fnhum.2021.749017 ↗
8. EMG Contamination of EEG: Spectral and Topographical Characteristics10.1016/S1388-2457(03)00093-2 ↗
9. Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises10.1109/ACCESS.2018.2890335 ↗
10. Isolating Gait-Related Movement Artifacts in Electroencephalography during Human Walking10.1088/1741-2560/12/4/046022 ↗
11. EEG Applications for Sport and Performance10.1016/j.ymeth.2008.07.006 ↗
12. Effects of Attention and Precision of Exerted Force on Beta Range EEG-EMG Synchronization during a Maintained Motor Contraction Task10.1016/S1388-2457(01)00722-2 ↗
13. Jiang, X., Bian, G.-B., and Tian, Z. (2019). Removal of Artifacts from EEG Signals: A Review. Sensors, 19.10.3390/s19050987 ↗
14. Reis, P.M.R., Hebenstreit, F., Gabsteiger, F., Von Tscharner, V., and Lochmann, M. (2014). Methodological Aspects of EEG and Body Dynamics Measurements during Motion. Front. Hum. Neurosci., 8.10.3389/fnhum.2014.00156 ↗
15. Artifact Subspace Reconstruction: A Candidate for a Dream Solution for EEG Studies, Sleep or Awake10.1093/sleep/zsad241 ↗
16. Juggler’s ASR: Unpacking the Principles of Artifact Subspace Reconstruction for Revision toward Extreme MoBI10.1016/j.jneumeth.2025.110465 ↗
17. Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings10.1109/TBME.2019.2930186 ↗
18. Mavros, P., Wälti, M.J., Nazemi, M., Ong, C.H., and Hölscher, C. (2022). A Mobile EEG Study on the Psychophysiological Effects of Walking and Crowding in Indoor and Outdoor Urban Environments. Sci. Rep., 12.10.1038/s41598-022-20649-y ↗
19. A Walk in the Park? Characterizing Gait-related Artifacts in Mobile EEG Recordings10.1111/ejn.14965 ↗
20. Ledwidge, P.S., McPherson, C.N., Faulkenberg, L., Morgan, A., and Baylis, G.C. (2025). A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running. Sensors, 25.10.3390/s25154810 ↗
21. Downey, R.J., and Ferris, D.P. (2023). iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG. Sensors, 23.10.3390/s23198214 ↗
22. Gonsisko, C.B., Ferris, D.P., and Downey, R.J. (2023). iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG. Sensors, 23.10.3390/s23020928 ↗
23. Gait speed-related changes in electrocortical activity in younger and older adults10.1152/jn.00544.2024 ↗
24. Arpaia, P., De Luca, M., Di Marino, L., Duran, D., Gargiulo, L., Lanteri, P., Moccaldi, N., Nalin, M., Picciafuoco, M., and Robbio, R. (2025). A Systematic Review of Techniques for Artifact Detection and Artifact Category Identification in Electroencephalography from Wearable Devices. Sensors, 25.10.3390/s25185770 ↗
25. A Comprehensive Review of Deep Learning Models for Denoising EEG Signals: Challenges, Advances, and Future Directions10.1007/s42452-025-07808-2 ↗
26. Dual-Electrode Motion Artifact Cancellation for Mobile Electroencephalography10.1088/1741-2552/aad7d7 ↗
27. Nordin, A.D., Hairston, W.D., and Ferris, D.P. (2019). Human Electrocortical Dynamics While Stepping over Obstacles. Sci. Rep., 9.10.1038/s41598-019-41131-2 ↗
28. Application of Tripolar Concentric Electrodes and Prefeature Selection Algorithm for Brain–Computer Interface10.1109/TNSRE.2007.916303 ↗
29. Tripolar Concentric Ring Electrodes for Capturing Localised Electroencephalography Signals during Sleep10.1111/jsr.14203 ↗
30. Comparison of Tripolar and Traditional EEG Recording of the Visual Evoked Potential10.1016/j.visres.2025.108594 ↗
31. Tri-Polar Concentric Ring Electrode Development for Laplacian Electroencephalography10.1109/TBME.2005.863887 ↗
32. Motion and Muscle Artifact Removal Validation Using an Electrical Head Phantom, Robotic Motion Platform, and Dual Layer Mobile EEG10.1109/TNSRE.2020.3000971 ↗
33. Hairston, W.D., Slipher, G.A., and Yu, A.B. (2016). Ballistic Gelatin as a Putative Substrate for EEG Phantom Devices. arXiv.
34. Richer, N., Downey, R.J., Nordin, A.D., Hairston, W.D., and Ferris, D.P. (2019, January 20–23). Adding Neck Muscle Activity to a Head Phantom Device to Validate Mobile EEG Muscle and Motion Artifact Removal. Proceedings of the 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA.10.1109/NER.2019.8716959 ↗
35. Symeonidou, E.-R., Nordin, A., Hairston, W., and Ferris, D. (2018). Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion. Sensors, 18.10.3390/s18041073 ↗
36. Yu, A., and Hairston, W. (2025, November 12). Open EEG Phantom. 2019. Available online: https://doi.org/10.17605/OSF.IO/QRKA2.
37. Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex10.1109/TBME.2019.2921766 ↗
38. A Lower Limb Phantom for Simulation and Assessment of Electromyography Technology10.1109/TNSRE.2019.2944297 ↗
39. EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis10.1016/j.jneumeth.2003.10.009 ↗
40. Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations10.1007/s12021-022-09581-8 ↗
41. Parameterizing Neural Power Spectra into Periodic and Aperiodic Components10.1038/s41593-020-00744-x ↗
42. Exploring the Reliability and Sensitivity of the EEG Power Spectrum as a Biomarker10.1016/j.ijpsycho.2020.12.002 ↗
43. Levin, A.R., Naples, A.J., Scheffler, A.W., Webb, S.J., Shic, F., Sugar, C.A., Murias, M., Bernier, R.A., Chawarska, K., and Dawson, G. (2020). Day-to-Day Test-Retest Reliability of EEG Profiles in Children With Autism Spectrum Disorder and Typical Development. Front. Integr. Neurosci., 14.10.3389/fnint.2020.00021 ↗
44. Schlink, B.R., Nordin, A.D., and Ferris, D.P. (2020). Human Myoelectric Spatial Patterns Differ among Lower Limb Muscles and Locomotion Speeds. Physiol. Rep., 8.10.14814/phy2.14652 ↗
45. Improvement of Spatial Selectivity and Decrease of Mutual Information of Tri-Polar Concentric Ring Electrodes10.1016/j.jneumeth.2007.06.007 ↗
46. Tripolar Concentric EEG Electrodes Reduce Noise10.1016/j.clinph.2019.10.022 ↗
47. Improved Spatial Resolution of Electroencephalogram Using Tripolar Concentric Ring Electrode Sensors10.1155/2020/6269394 ↗
48. Melnik, A., Legkov, P., Izdebski, K., Kärcher, S.M., Hairston, W.D., Ferris, D.P., and König, P. (2017). Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?. Front. Hum. Neurosci., 11.10.3389/fnhum.2017.00150 ↗
49. Delorme, A. (2023). EEG Is Better Left Alone. Sci. Rep., 13.10.1038/s41598-023-27528-0 ↗
50. Klug, M., Berg, T., and Gramann, K. (2024). Optimizing EEG ICA Decomposition with Data Cleaning in Stationary and Mobile Experiments. Sci. Rep., 14.10.1038/s41598-024-64919-3 ↗
51. Identifying Key Factors for Improving ICA-based Decomposition of EEG Data in Mobile and Stationary Experiments10.1111/ejn.14992 ↗
52. High and Dry? Comparing Active Dry EEG Electrodes to Active and Passive Wet Electrodes10.1111/psyp.12536 ↗
53. A Primer on EEG Spectrograms10.1097/WNP.0000000000000736 ↗
54. Clinical Applications of EEG Power Spectra Aperiodic Component Analysis: A Mini-Review10.1016/j.clinph.2022.08.010 ↗
55. Robust Power Spectral Estimation for EEG Data10.1016/j.jneumeth.2016.04.015 ↗
56. Source Localization of High-Frequency Activity in Tripolar Electroencephalography of Patients with Epilepsy10.1016/j.yebeh.2019.106519 ↗
57. Brain Electric Source Imaging: Scalp Laplacian Mapping and Cortical Imaging