Enhancing an EEG Noise Removal Web Application: From Code to User Interface
Channel: BioniChaos
Date: 2023-11-08

Wavelet Based EEG Noise Removal: Time Frequency Web App Development Part 3

In this video, I delve into the development of an EEG noise removal project, focusing on updating and troubleshooting various aspects of the web application. I start by examining the current state of the project, highlighting the transition from LV to Python and the need to republish the application. Throughout the video, I work on improving the user interface, specifically adjusting the layout to ensure all elements fit comfortably within the screen. I also tackle coding challenges, such as implementing signal-to-noise ratio (SNR) and mean square error (MSE) calculations, and ensuring these values are displayed correctly. Additionally, I explore responsive design techniques to make the application adaptable to different screen sizes. The video is a comprehensive look at the iterative process of web development, from coding in HTML, JavaScript, and Python to refining the user experience.

(00:03) Starting EEG noise removal project development
(01:03) Transition from LV to Python
(02:00) Original signal and noise analysis
(03:08) Beginning HTML file adjustments
(04:52) JavaScript and CSS modifications
(06:06) Python Flask application adjustments
(11:29) Preparing to publish the application
(13:20) Implementing SNR and MSE calculations
(21:27) Responsive design for user interface
(27:00) Specific code suggestions for UI improvement
(31:07) CSS Flexbox for layout adaptation
(35:20) Finalizing outputs and preparing for publication

#WebDevelopment #EEGNoiseRemoval #Python #JavaScript #HTML #CSS #ResponsiveDesign #UserInterface #Coding #WebAppDevelopment #SignalProcessing #SNR #MSE #Flask #softwaredevelopment 

The tools I develop are available on https://bionichaos.com 
You can support my work on https://patreon.com/bionichaos