1. Preprocessing – Bandpass Filtering
Tasks:
Experiment with deep learning models using specific bandpass frequency ranges:
7–32 Hz.
7–40 Hz.
Analyze if narrowing or expanding the frequency range improves model accuracy.
Validate these frequency ranges' impact on noise reduction and feature enhancement in EEG signals.
2. Preprocessing – ICA (Independent Component Analysis)
Tasks:
Expand the current ICA implementation:
Add steps for removing unnecessary components after applying ICA.
Reconstruct the EEG signal post-component removal.
Research missing steps in the current implementation of ICA.
Investigate alternate ICA techniques or additional steps for better artifact removal and improved signal quality.
Test the effect of these enhancements on model performance.
3. Research Contribution – Channel Selection
Tasks:
Re-evaluate the algorithm for EEG channel selection:
The current method includes all channels, reducing its effectiveness.
Explore and implement more robust algorithms tailored to EEG signal analysis.
Focus on algorithms that identify and select the most relevant channels for the task.
Assess how improved channel selection impacts the final results.
4. Comparison Metrics – Before and After Preprocessing
Tasks:
Address the low PSNR (Peak Signal-to-Noise Ratio):
Investigate if it is caused by incomplete ICA preprocessing.
Modify preprocessing steps accordingly.
Compute and analyze SNR (Signal-to-Noise Ratio) before and after preprocessing to measure improvement.
Consider using additional comparison metrics (e.g., MSE, SSIM) to provide a comprehensive evaluation of preprocessing.
5. Pretrained Model Results
Tasks:
Debug and refine the code for the pretrained models:
Currently, the results table shows identical accuracy, precision, and recall for all models, suggesting errors in implementation or evaluation.
Cross-validate the models with updated datasets and preprocessing methods.
Generate new performance metrics and plots reflecting improvements.
6. Final Model Architecture
Tasks:
Design a novel model architecture combining:
Pretrained models.
Custom-designed components tailored to EEG analysis.
Implement and test this combined architecture.
Fine-tune hyperparameters for optimal performance on the EEG dataset.
Additional Suggestions:
Regularly benchmark improvements using updated metrics.
Document all changes for reproducibility and comparison