Results for “electrophysiology”

Process and analyze physiological signals including ECG, EEG, EDA, RSP, PPG, EMG, and EOG using Python.

Processes and analyzes multiple physiological signals (ECG, respiration, EDA, EMG, PPG, EOG) together using NeuroKit2, including cross-signal features like RSA and event-related analysis.

Extracts and analyzes firmware images using binwalk to identify embedded filesystems, compressed archives, bootloaders, kernel images, and cryptographic material. Covers entropy analysis, recursive extraction, filesystem mounting, and string analysis for credential and configuration discovery.

Parse Windows forensic artifacts including registry, prefetch, shellbags, MFT, and event logs using Eric Zimmerman's tools and analyze results in Timeline Explorer.

Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.

Manipulate phylogenetic trees, detect evolutionary events, integrate NCBI taxonomy, and create publication-quality visualizations using the ETE toolkit.

Performs differential expression analysis on label-free quantitative (LFQ) proteomics data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.

Guides analysis of Neuropixels recordings from raw data to curated units, covering preprocessing, motion correction, spike sorting, quality metrics, and export.