Update README.md

README.md

@@ -9,5 +9,62 @@ 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:
+
+[![EEG Signal Processing Demo](https://img.youtube.com/vi/_vWz-p26roY/maxresdefault.jpg)](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