TAPAS - Translational Algorithms for Psychiatry-Advancing Science

EyeLoop is a Python 3-based eye-tracker tailored specifically to dynamic, closed-loop experiments on consumer-grade hardware.

Python package for digital phenotyping data synchronization and acquisition

Artificial Intelligence and Machine Learning to help find scientific research and filter relevant content

NUNIX (Neural UNIX) is an experimental operating system project that can act as a second brain for the human mind. It is far from complete.

An interactive brain receptive field and stimulation viewer.

biology / medicine / health papers organized in markdown files

Assorted machine learning implementations for medical data.

This tool allows providers to track cognitive assessments for high-risk MCI patients. This open-source project is designed to provide a standarized modular environment for digital psychometric analysis. The goal of this tool is to assist PCPs, Specialists, and Researchers obtain assessment data outside of the hospital environment at set frequenc…

Machine Learning for Neuroscience Module, Spring 2023 (https://www.imperial.ac.uk/study/courses/postgraduate-taught/translational-neuroscience/)

AI4QI, Khosravani Lab, Using AI for quality improvement in neurology

Analysis code and results for the study "Association between stroke and psychosis across four nationally representative epidemiological studies"

A tool for automatic measurements of axon lengths, the nerve fibers connecting neurons in a brain.

Code and slides related to salary expectations for academic neurologists

Central code repository for UNICORN Lab at NYU Langone Health. Our goal is to better understand the mechanisms through which our environments impact our brain health and then design, develop, and implement human-centered interventions that aim to improve universal brain health.

💊 Code and results of a statistical study of databases from Virgen del Rocío's hospital concerning to strokes episodes and how different risk factors affect the condition.

Matlab-designed algorithm implemented with Machine Learning methods. The purpose was to use collected signal information to discriminate between right-lobe, left-lobe, and passive activity.