Optimize EEG Channel Selection
Budget: $10 – $30 USD
I’m extending the public CHB-MIT seizure-detection notebook (see: https://www.kaggle.com/code/masahirogotoh/chb-mit-eeg-dataset-seizure-detection-demo). Before the deep-learning section I want a solid, publishable channel-reduction stage that genuinely boosts accuracy, so that fewer electrodes are needed without sacrificing performance.
So far I have implemented four recent meta-heuristics—GASO, EVO, Hippopotamus Optimization and the Botox Optimization Algorithm—using a Random Forest classifier as the fitness evaluator (fitness = classification accuracy on the seizure task). Results are promising, but I need a specialist to refine and stabilise this optimisation block and add a clear element of novelty.
Key tasks
• Convert the current continuous search space to an effective binary representation, OR introduce an Opposition-Based Learning scheme (feel free to combine both if it clearly improves results).
• Re-code or tune the existing algorithms so that accuracy either holds steady or improves when the channel count drops.
• Keep the code modular so it slots back into the original notebook immediately before the LSTM/CNN section.
• Provide concise in-line comments and a short README explaining parameters, assumptions and how to reproduce results end-to-end.
Also I need a table to compare the accuracy before enhancing and after enhancing.
Acceptance
The optimised subset must reach equal or higher accuracy than the full-channel baseline reported in the original notebook, measured with five-fold cross-validation on the same train/test split. Deliverables are the updated .ipynb, any auxiliary .py files, and a brief markdown summary of findings and next steps.
So far I have implemented four recent meta-heuristics—GASO, EVO, Hippopotamus Optimization and the Botox Optimization Algorithm—using a Random Forest classifier as the fitness evaluator (fitness = classification accuracy on the seizure task). Results are promising, but I need a specialist to refine and stabilise this optimisation block and add a clear element of novelty.
Key tasks
• Convert the current continuous search space to an effective binary representation, OR introduce an Opposition-Based Learning scheme (feel free to combine both if it clearly improves results).
• Re-code or tune the existing algorithms so that accuracy either holds steady or improves when the channel count drops.
• Keep the code modular so it slots back into the original notebook immediately before the LSTM/CNN section.
• Provide concise in-line comments and a short README explaining parameters, assumptions and how to reproduce results end-to-end.
Also I need a table to compare the accuracy before enhancing and after enhancing.
Acceptance
The optimised subset must reach equal or higher accuracy than the full-channel baseline reported in the original notebook, measured with five-fold cross-validation on the same train/test split. Deliverables are the updated .ipynb, any auxiliary .py files, and a brief markdown summary of findings and next steps.