In this project, we have designed an interactive dashboard to visualize case study results. The dashboard is a website built using the Flask framework and enhanced with interactive graph features from the Dash library. This combination allows users to efficiently and effectively explore and analyze case study data.
The interactive dashboard's architecture is structured to provide a seamless user experience. Users access the website and interact with Dash Plotly components, such as graphs and input elements like combo boxes for selecting datasets. Dash Plotly serves as the main user interface, facilitating interaction with the system.
Diagram of Dashboard Architecture
User Interaction with Dash Plotly:
Users access the website and interact with the visual components.
They can select different datasets and elements using input tools.
Dash Plotly and Flask Integration:
Dash Plotly sends requests to Flask, which manages the website's backend.
Flask fetches the necessary data using MNE Tools, which processes EEG data stored in raw.fif files.
The processed data is then sent back to Flask.
Data Visualization:
Flask passes the processed data to Dash Plotly.
Dash Plotly presents the data in the form of interactive and informative graphs.
Users can interact with these graphs to gain a deeper understanding of the analyzed data.
This architecture allows for an efficient flow of data from storage to user interaction, providing a robust platform for data visualization. By leveraging Flask for backend management and Dash Plotly for front-end interaction, the dashboard offers a powerful tool for presenting and exploring EEG data visually. This makes it easier for users to comprehend complex datasets and derive meaningful insights.
Documentation
