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Interpretability in Neurotechnology: Using Explainable AI to Identify Biomarkers in Deep Brain Stimulation
Sankaraleengam Alagapan · Georgia Institute of Technology, United States
Subcallosal cingulate stimulation for resistant depression needs objective biomarkers beyond stress-sensitive clinical ratings. Sankaraleengam Alagapan uses variational autoencoders to address high-dimensional longitudinal local-field potentials and unreliable labels. Supervised models track depressive states; a generative causal explainer identifies interpretable, low-dimensional features, advancing actionable biomarkers for stimulation management.
Series: Frontier Series 2026 — Örebro University and Lund UniversityComputational NeuroscienceMachine Learning