Spaces:
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| title: BCI | |
| emoji: 🏃 | |
| colorFrom: indigo | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 4.36.1 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # EEG Signal Processing and Classification | |
| This Gradio Space allows you to process and classify EEG signals. You can upload EEG data, label it, preprocess the data, and train a machine learning model directly in your browser. | |
| ## Overview | |
| This project provides an interface for: | |
| 1. Uploading and previewing EEG data. | |
| 2. Labeling data segments. | |
| 3. Preprocessing data to extract features. | |
| 4. Training a machine learning model. | |
| 5. Downloading the trained model and scaler. | |
| ## Demo | |
| Check out the video demonstration below to see how to use the interface: | |
| [](https://www.youtube.com/watch?v=_vWz-p26roY) | |
| ## Full Instructable | |
| For a detailed step-by-step guide, visit Instructable page [here](https://www.instructables.com/Controlling-Video-Game-Using-Brainwaves-EEG/). | |
| ## Usage | |
| ### Uploading Data | |
| 1. Click on the "Upload CSV File" button to upload your EEG data. | |
| 2. Preview the uploaded data in the "Data Preview" section. | |
| ### Labeling Data | |
| 1. Enter the start index, end index, and label for each segment in the "Ranges for Labeling" section. | |
| 2. Click on the "Label Data" button to apply the labels. | |
| ### Training the Model | |
| 1. Click on the "Train Model" button to preprocess the data and train the model. | |
| 2. Download the trained model and scaler using the provided links. | |
| ## File Descriptions | |
| - `app.py`: Contains the Gradio interface and main application logic. | |
| - `requirements.txt`: Lists the dependencies required to run the project. | |
| - `model.pkl`: The trained machine learning model (generated after training). | |
| - `scaler.pkl`: The scaler used to preprocess the data (generated after training). | |
| ## License | |
| This project is licensed under the apache-2.0. | |
| ## Acknowledgments | |
| - Special thanks to the contributors and the open-source community. | |
| - Thanks to the authors of the libraries used in this project. | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |