video1.0<iframe src="https://www.loom.com/embed/dd5a483e554a4fe3b87c603fe1dfc135" frameborder="0" width="1672" height="1254" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12541672Loomhttps://www.loom.com12541672https://cdn.loom.com/sessions/thumbnails/dd5a483e554a4fe3b87c603fe1dfc135-de27d6ef33bfed07.gif1116.325In this video, I walk you through our recent work on adaptive chirplet analysis, which we applied to EEG recordings to study brain wave patterns during sleep, particularly focusing on sub-20 Hz frequencies. I demonstrate how we decompose complex signals into chirplets, achieving a reconstruction energy of 90% with just 10 chirplets, significantly reducing the data complexity. I also showcase our CLI workbench and a P5JS visualization tool to analyze the results. Please subscribe to my channel for updates, as we continue to optimize our library for real-time analysis using GPU acceleration. Your feedback and engagement will be invaluable as we move forward with this project.