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This repository contains a Real-Time Brain-Computer Interface (BCI) System for stress monitoring and control using EEG signals. It captures live brainwave data, detects stress levels using machine learning, and enables real-time responses for health, wellness, or interactive applications.

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MIT license
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WashifAkhtar/Real-Time_EEG_Stress_Monitoring_and_Control

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arduino_code
arduino_code
 
 
data
data
 
 
models
models
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 
Train_CNN_Model_v3.ipynb
Train_CNN_Model_v3.ipynb
 
 
arduino_control.py
arduino_control.py
 
 
arduino_control_with_log.py
arduino_control_with_log.py
 
 
arduino_stress_log.csv
arduino_stress_log.csv
 
 
arduino_stress_plot.png
arduino_stress_plot.png
 
 
game_control.py
game_control.py
 
 
game_control_log.csv
game_control_log.csv
 
 
game_control_stress_plot.png
game_control_stress_plot.png
 
 
game_control_with_log.py
game_control_with_log.py
 
 
stress_detection.py
stress_detection.py
 
 
stress_detection_realtime.py
stress_detection_realtime.py
 
 
stress_detection_with_log.py
stress_detection_with_log.py
 
 
stress_log.csv
stress_log.csv
 
 
stress_log_20250618_121131.csv
stress_log_20250618_121131.csv
 
 
stress_log_20250618_124649.csv
stress_log_20250618_124649.csv
 
 
stress_over_time.png
stress_over_time.png
 
 

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Real-Time_EEG_Stress_Monitoring_and_Control

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This repository contains a Real-Time Brain-Computer Interface (BCI) System for stress monitoring and control using EEG signals. It captures live brainwave data, detects stress levels using machine learning, and enables real-time responses for health, wellness, or interactive applications.

Topics

python machine-learning real-time signal-processing eeg mental-health bci brain-computer-interface biofeedback bci-systems neurotechnology stress-detection brainwaves healthcare-ai wearable-tech

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