EEG Seizure Detection & Prediction
Budget: $10 – $30 USD
I have a sizeable EEG dataset that needs to be transformed into a working epileptic-seizure detection and short-term prediction pipeline. The workflow I have in mind is clear:
• Data preprocessing: begin with classic EEG Filtering and follow with signal Normalization to standardise every trial.
• Feature extraction: apply a Wavelet Transform to capture the relevant time–frequency patterns that precede a seizure.
• Dimensionality reduction: reduce the resulting feature space intelligently so the model is both fast and interpretable.
• Modelling: my first choice is a Random Forest classifier/regressor for both detection and prediction tasks; however, if you can show convincing evidence that a complementary deep-learning block will boost performance without sacrificing interpretability, I am open to discussion.
• Explainable AI: integrate SHAP or LIME so each prediction can be traced back to the contributing EEG channels, scales, or time-windows.
• Performance evaluation: deliver detailed metrics (accuracy, sensitivity, specificity, F1, ROC-AUC, false-alarm rate per hour) on a solid cross-validation split and, if possible, an unseen hold-out set.
Deliverables must include
– Clean, well-commented source code (Python preferred: NumPy, Pandas, MNE, scikit-learn, SHAP/LIME, etc.)
– A concise technical report that documents preprocessing choices, feature engineering, modelling decisions, and performance tables/plots
– Reproducible experiments packaged in notebooks or scripts with clear run instructions
– A read-me explaining environment setup and how to deploy or further train the system.
I value transparent, modular code, reproducibility, and plain-language explanations of the model’s decisions. If this matches your expertise, let’s make EEG more predictable together.
• Data preprocessing: begin with classic EEG Filtering and follow with signal Normalization to standardise every trial.
• Feature extraction: apply a Wavelet Transform to capture the relevant time–frequency patterns that precede a seizure.
• Dimensionality reduction: reduce the resulting feature space intelligently so the model is both fast and interpretable.
• Modelling: my first choice is a Random Forest classifier/regressor for both detection and prediction tasks; however, if you can show convincing evidence that a complementary deep-learning block will boost performance without sacrificing interpretability, I am open to discussion.
• Explainable AI: integrate SHAP or LIME so each prediction can be traced back to the contributing EEG channels, scales, or time-windows.
• Performance evaluation: deliver detailed metrics (accuracy, sensitivity, specificity, F1, ROC-AUC, false-alarm rate per hour) on a solid cross-validation split and, if possible, an unseen hold-out set.
Deliverables must include
– Clean, well-commented source code (Python preferred: NumPy, Pandas, MNE, scikit-learn, SHAP/LIME, etc.)
– A concise technical report that documents preprocessing choices, feature engineering, modelling decisions, and performance tables/plots
– Reproducible experiments packaged in notebooks or scripts with clear run instructions
– A read-me explaining environment setup and how to deploy or further train the system.
I value transparent, modular code, reproducibility, and plain-language explanations of the model’s decisions. If this matches your expertise, let’s make EEG more predictable together.