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Semi-Automatic Multi-Stage Artifact Removal in EEG During Subthreshold GVS: A Machine Learning Approach for Neuromodulation Studies

Authors :

Mahdi Babaei¹ Sepideh Hajipour Sardouie² Martin Keung³ Varsha Sreenivasan⁴ Hanaa Diab⁵ Maryam S. Mirian⁶ Martin J. McKeown⁷

1- دانشکده برق‌و‌کامپیوتر، دانشگاه تهران 2- University of British Columbia 3- University of British Columbia 4- University of British Columbia 5- University of British Columbia 6- University of British Columbia 7- University of British Columbia

Keywords :

Parkinson’s disease،Galvanic vestibular stimulation،Neuromodulation،Artifact removal،EEG،biomarkers،Machine learning

Abstract :

Parkinson’s disease (PD) is characterized by widespread disruptions in neural oscillations and network dynamics, which can be captured through resting-state EEG biomarkers. Galvanic vestibular stimulation (GVS) has emerged as a promising noninvasive neuromodulation technique to modulate these neural patterns. However, EEG recordings during GVS are severely contaminated by high-amplitude stimulation artifacts, especially when exploring a wide range of stimulation protocols. In this study, we designed a data acquisition protocol involving 304 distinct subthreshold GVS waveforms, each with a unique temporal profile, to investigate their effects on brain activity. These stimuli induced strong artifacts in the EEG signal, particularly during the stimulation interval. To recover clean EEG signals, we developed a multi-stage preprocessing pipeline combining regression-based artifact suppression, canonical correlation analysis (CCA), and independent component analysis (ICA), supported by machine learning classifiers for automatic detection and removal of GVS, EOG, and EMG artifacts. We evaluated the effectiveness of this pipeline through classification of EEG signals from PD patients and healthy controls across three temporal segments: pre-stimulation (Pre-stim), stimulation (Stim), and post-stimulation (Post-stim). Despite the intense artifacts in the Stim interval, classification accuracy reached 82.46%, closely matching the performance in Pre-stim (85.06%) and Post-stim (91.67%) intervals. This confirms that the artifact removal process successfully preserved disease-relevant neural information even during active stimulation. Beyond classification, we conducted additional evaluations including temporal consistency analysis of biomarkers, correlation of model coefficients across intervals, and visual inspection of signal quality. These assessments demonstrated that the cleaned EEG signals retained physiologically meaningful patterns and stable biomarker profiles across time. Our findings show that EEG signals recorded during GVS can be reliably cleaned and analyzed, enabling rapid screening of stimulation protocols and paving the way for personalized neuromodulation strategies in Parkinson’s disease.