Stress Detection through EEG Signals
Stress Detection through EEG Signals designed through Django web framework and using python programming language. User have to run main file through terminal or code editor and it will provide a URL, user have to paste that URL on any browser and it will open a web page, it contain project details and web page integrated with machine learning model. It contains Home Page, About Us, Start Detection.
It is designed through Django web framework and using python programming language. User have to run main file through terminal or code editor and it will provide a URL, user have to paste that URL on any browser and it will open a web page, it contain project details and web page integrated with machine learning model. It contains Home Page, About Us, Start Detection.
When user click on start detection page then it will open a machine learning model, user have to provide inputs to model:
- 1. Input from TP9
- 2. Input from TP10
- 3. Input from AF8
- 4. Input from AF7
- 5. Input from Right Aux
Emotion Recognition using EEG Signals
According to user inputs to machine learning model it will predict the output and it will also stored the data in database. EEG Based Stress Detection Machine Learning model is trained on large dataset of CSV file and according to dataset, user have to provide input.
To build an EEG-based emotion recognition system with Python, we use advanced techniques to analyze brainwave signals and machine learning methods. Python’s flexibility assists us in understanding these signals and recognizing emotional states.
Creating an EEG-based emotion recognition system using Python involves several key steps. Initially, EEG data acquisition is performed using specialized hardware. Pre-processing techniques such as noise removal and signal filtering are then applied to enhance data quality. Feature extraction methods extract relevant patterns from EEG signals. Machine learning algorithms, commonly used with Python libraries like scikit-learn or TensorFlow, learn from these features to categorize emotional states. Through extensive testing and validation, the system is refined to ensure accuracy and reliability across different emotional situations, making it ready for real-world use.
Stepwise explanation of EEG based Stress Recognition using Python:-
1. EEG data acquisition using specialized hardware.
2. Pre-processing for noise reduction and signal enhancement.
3. Feature extraction to capture relevant patterns.
4. Implementation of machine learning algorithms.
5. Training classifiers using Python libraries like scikit-learn or TensorFlow.
6. Rigorous testing and validation for accuracy.
7. Deployment for practical applications in diverse contexts.
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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