I developed a Deep Learning model for analyzing and classifying EEG brain signals using a hybrid neural network architecture.
The project combines Convolutional Neural Networks (CNN) for spatial feature extraction and Recurrent Neural Networks (RNN) for capturing temporal patterns in EEG signal data.
Project Workflow
The implementation includes several stages:
• Data preprocessing and normalization
• Feature extraction from EEG signals
• Building a hybrid CNN-RNN Deep Learning model
• Model training and validation
• Performance evaluation using multiple metrics
• Visualization of training results and model performance
Model Evaluation
The model performance was evaluated using:
Accuracy
Precision
Recall
F1 Score
Confusion Matrix
Technologies Used
Python
TensorFlow
Keras
NumPy
Matplotlib
Scikit-learn
Applications
This type of system can be used in multiple domains such as:
Brain Computer Interface (BCI)
Medical signal analysis
Neuroscience research
Artificial Intelligence applications in healthcare
