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SUMMARY:Efficient artifact removal for adaptive deep brain stimulation - T
 zu-Chi Liu (National Taiwan University)
DTSTART:20251203T112000Z
DTEND:20251203T120500Z
UID:TALK241003@talks.cam.ac.uk
DESCRIPTION:Adaptive deep brain stimulation (aDBS) modulates neural activi
 ty based on symptom-related biomarkers\, promising improved efficacy and e
 nergy efficiency over conventional DBS. A persistent challenge\, however\,
  is stimulation-induced artifacts that corrupt neural recordings and compr
 omise real-time biomarker extraction.\nWe present SMARTA+\, a computationa
 lly efficient algorithm for removing both periodic stimulation and transie
 nt DC artifacts. SMARTA+ leverages modern random matrix theory to model lo
 cal field potentials as noise with a separable covariance structure\, and 
 employs an approximate nearest neighbor scheme to achieve real-time perfor
 mance. Using semi-real aDBS and Parkinson&rsquo\;s patient data\, SMARTA+ 
 outperforms existing methods in artifact suppression\, preserves spectral&
 ndash\;temporal features from beta to high-frequency oscillations\, and im
 proves beta-burst detection. These results highlight SMARTA+ as a mathemat
 ically principled and practical tool for advancing closed-loop neuromodula
 tion.
LOCATION:Seminar Room 1\, Newton Institute
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