Sebastián Castaño-Candamil

Latest

• A pilot study on data-driven adaptive deep brain stimulation in chronically implanted essential tremor patients
• Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson’s disease
• A Simulated Environment for Studying Partial Observability in Novel Adaptive Deep Brain Stimulation
• A Simulated Environment for Early Development Stages of Reinforcement Learning Algorithms for Closed-Loop Deep Brain Stimulation
• An Easy-to-Use and Fast Assessment of Patient-Specific DBS-induced Changes in Hand Motor Control in Parkinson's Disease
• Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods
• Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems
• 1-F-54 Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
• 2-A-2 Adaptive deep brain stimulation: Optimization of treatment in essential tremor using electrocorticography data
• Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
• Closed-Loop Deep Brain Stimulation System for an Animal Model of Parkinson's Disease: A Pilot Study
• Subspace Decomposition in the Frequency Domain
• BCI-Approach for Cognitive Rehabilitation in Stroke: Pilot Data from Patient with Spatial Neglect
• EP 65. DBS-induced alpha desynchronization in the subthalamic nucleus of PD patients
• ERP Features Correlate with Reaction Time in a Covert-Attention Task
• Pre-Trial EEG-based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task
• Relevant Frequency Estimation in EEG Recordings for Source Power Co-Modulation
• P186. Correlates to influence user performance in a hand motor rehabilitation task
• Solving the EEG inverse problem based on space-time-frequency structured sparsity constraints