A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running
Electroencephalography (EEG) is the only brain imaging method light enough and with the temporal precision to assess electrocortical dynamics during human locomotion. However, head motion during whole-body movements produces artifacts that contaminate the EEG and reduces ICA decomposition quality. W...
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MDPI AG
2025-08-01
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| author | Patrick S. Ledwidge Carly N. McPherson Lily Faulkenberg Alexander Morgan Gordon C. Baylis |
| author_facet | Patrick S. Ledwidge Carly N. McPherson Lily Faulkenberg Alexander Morgan Gordon C. Baylis |
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| description | Electroencephalography (EEG) is the only brain imaging method light enough and with the temporal precision to assess electrocortical dynamics during human locomotion. However, head motion during whole-body movements produces artifacts that contaminate the EEG and reduces ICA decomposition quality. We compared commonly used motion artifact removal approaches for reducing the motion artifact from the EEG during running and identifying stimulus-locked ERP components during an adapted flanker task. EEG was recorded from young adults during dynamic jogging and static standing versions of the Flanker task. Motion artifact removal approaches were evaluated based on their ICA’s component dipolarity, power changes at the gait frequency and harmonics, and ability to capture the expected P300 ERP congruency effect. Preprocessing the EEG using either iCanClean with pseudo-reference noise signals or artifact subspace reconstruction (ASR) led to the recovery of more dipolar brain independent components. In our analyses, iCanClean was somewhat more effective than ASR. Power was significantly reduced at the gait frequency after preprocessing with ASR and iCanClean. Finally, preprocessing using ASR and iCanClean also produced ERP components similar in latency to those identified in the standing flanker task. The expected greater P300 amplitude to incongruent flankers was identified when preprocessing using iCanClean. ASR and iCanClean may provide effective preprocessing methods for reducing motion artifacts in human locomotion studies during running. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-08-01 |
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| spelling | doaj-art-0d18b97391034d64bc4be9b28c95bda52025-08-20T03:02:56ZengMDPI AGSensors1424-82202025-08-012515481010.3390/s25154810A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground RunningPatrick S. Ledwidge0Carly N. McPherson1Lily Faulkenberg2Alexander Morgan3Gordon C. Baylis4Department of Psychological Sciences, Western Kentucky University, 1906 College Heights Blvd., Bowling Green, KY 42101, USADepartment of Psychological Sciences, Western Kentucky University, 1906 College Heights Blvd., Bowling Green, KY 42101, USADepartment of Psychological Sciences, Western Kentucky University, 1906 College Heights Blvd., Bowling Green, KY 42101, USADepartment of Allied Health, Sport & Wellness, Baldwin Wallace University, 275 Eastland Rd., Berea, OH 44017, USADepartment of Psychological Sciences, Western Kentucky University, 1906 College Heights Blvd., Bowling Green, KY 42101, USAElectroencephalography (EEG) is the only brain imaging method light enough and with the temporal precision to assess electrocortical dynamics during human locomotion. However, head motion during whole-body movements produces artifacts that contaminate the EEG and reduces ICA decomposition quality. We compared commonly used motion artifact removal approaches for reducing the motion artifact from the EEG during running and identifying stimulus-locked ERP components during an adapted flanker task. EEG was recorded from young adults during dynamic jogging and static standing versions of the Flanker task. Motion artifact removal approaches were evaluated based on their ICA’s component dipolarity, power changes at the gait frequency and harmonics, and ability to capture the expected P300 ERP congruency effect. Preprocessing the EEG using either iCanClean with pseudo-reference noise signals or artifact subspace reconstruction (ASR) led to the recovery of more dipolar brain independent components. In our analyses, iCanClean was somewhat more effective than ASR. Power was significantly reduced at the gait frequency after preprocessing with ASR and iCanClean. Finally, preprocessing using ASR and iCanClean also produced ERP components similar in latency to those identified in the standing flanker task. The expected greater P300 amplitude to incongruent flankers was identified when preprocessing using iCanClean. ASR and iCanClean may provide effective preprocessing methods for reducing motion artifacts in human locomotion studies during running.https://www.mdpi.com/1424-8220/25/15/4810electroencephalography (EEG)event-related potentials (ERP)mobile brain imagingsignal processingindependent components analysis (ICA) |
| spellingShingle | Patrick S. Ledwidge Carly N. McPherson Lily Faulkenberg Alexander Morgan Gordon C. Baylis A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running Sensors electroencephalography (EEG) event-related potentials (ERP) mobile brain imaging signal processing independent components analysis (ICA) |
| title | A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running |
| title_full | A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running |
| title_fullStr | A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running |
| title_full_unstemmed | A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running |
| title_short | A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running |
| title_sort | comparison of approaches for motion artifact removal from wireless mobile eeg during overground running |
| topic | electroencephalography (EEG) event-related potentials (ERP) mobile brain imaging signal processing independent components analysis (ICA) |
| url | https://www.mdpi.com/1424-8220/25/15/4810 |
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