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Analysis of Emotion using EEG Headset - Stressed Detection

Serving for the course Machine Learning - IT3190E

This is a project of building a Brain Computer Interface (BCI) system to classify emotional stress state base on EEG signal.

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Overview

In the hospitality industry, pricing strategies play a crucial role in maximizing revenue and ensuring competitiveness. The ability to predict hotel room prices with accuracy is fundamental for both hotel management and customers. The problem lies in the inherent unpredictability of hotel prices, influenced by various dynamic factors such as demand fluctuations, market competition, seasonal trends, local events, customer preferences, and economic conditions. Traditionally, pricing has been a reactive process, with limited capacity to anticipate price fluctuations, leading to revenue loss or missed opportunities for both hoteliers and consumers. Therefore, an effective hotel price prediction system is needed to assist both sides in making informed decisions.

Requirement

The following libraries and frameworks are required for running the project:

• Python 3.10
• OpenCV
• Tensorflow
• Keras
• NumPy
• Matplotlib
• Scikit-learn
• skimage ...

To install these dependencies, you can use the provided requirements.txt file.

Example:

pip install -r requirements.txt

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Installation

1. Clone the repository:

   git clone https://github.com/sonho4ng/Mental-Stressed-Detection-using-EEG-Headset.git
   cd Mental-Stressed-Detection-using-EEG-Headset
   

2. Install dependencies:

   If you're using a requirements.txt file, run:

   pip install -r requirements.txt
   

   Alternatively, install required libraries individually using pip.

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User Instruction

• Run the Demo.py to run the UI for demonstration
• Run the Record.py to record the data (use when you have the EEG recording device)
• Each AI model is implemented in the Model folder, feel free to run each model for more detail.