Plamen Nikolov, PhD

Investigation of computational models engaged during auditory and spoken word speech processing, with potential applications for neuroscience-based speech therapy.

• Developed methods for EEG image acquisition and decoding of very fast (millisecond range) neural dynamics involving significant motion artifacts.
• Developed multivariate analysis pipelines using HPC clusters to analyze large neural image datasets (cut preprocessing time from 3 weeks to 2 days).
• Led interdisciplinary team investigating evidence of internal inverse and forward models in the brain.
• Secured independent funding (NIH T32 & TL1) and led grant proposals from concept to award.

Large-scale electrophysiology simulations to analyze and predict arrhythmia behavior using patient-specific models.

• Built MR-derived, patient-specific cardiac models and ran large-scale EP simulations.
• Delivered the first clinical trial using virtual heart EP simulations to inform ventricular tachycardia ablation planning (Nature Biomedical Engineering).
• Led two industry-academic partnerships to productionize finite-element solvers for biologically realistic heart models.

Built an end-to-end machine learning framework to model recovery trajectories in traumatic brain injury patients.

• Built connectome-based ML framework using 2-week post-injury fMRI from the TRACK-TBI cohort.
• Processed functional connectivity matrices to extract graph/network features.
• Trained elastic net, ridge, and gradient boosting models to predict Glasgow Outcome Scale-Extended (GOSE) at 3/6/12 months.

Design and implementation of non-invasive brain-computer interface technologies.

• Designed visual stimulus-based BCI system integrated within a real-time EEG pipeline.
• Wrote assembly-level firmware for deterministic timing and achieved four-class control for on-screen navigation.
• Developed algorithms for motor imagery BCI, validating timing determinism and signal integrity.
• Conducted empirical evaluations to optimize accuracy, false-activation rate, and user training time.