Kaggle Grasp-and-Lift Detection

Code in this repository can be used to train and sample both LSTM and CNN (quite experimental) models on Kaggle Grasp-and-Lift EEG Detection competition data.

I have no idea what it's leaderboard score is because I forgot about the entry deadline...

Notes

These models probably aren't performing really well. I had little knowledge in signal processing and EEG domain and I've not spent enough time on this competition to get satisfying results. Anyway, it was a great opportunity to learn how LSTMs work and how to use python for data processing.

Setup

After cloning the repo you should run setup.sh, which will prepare the directory structure and preprocess the data. It should be ready to work afterwards.

Main scripts

There are two other scripts attached:

• do_subject.sh num - trains an LSTM model for subject number num and evaluates it on validation set.
• do_submission.sh - trains an LSTM model for each subject separately, and generates both validation and submission files

Pipeline

First python scripts are used for preprocessing.

1. calc_mean_std.py applies low pass filter, calculates mean and std of the data and saves it in data/mean_std.pickle for future use.
2. modify_data.py applies preprocessing, selects some files for validation and saves them in data/preprocessed (validation files have .val appended to their name).
3. train_lstm.lua/train_conv.lua reads preprocessed data, puts it into Torch tensors and caches them in data/torch. Then it trains the corresponding model saving checkpoints in cv/ folder.
4. sample.lua recreates model from specified checkpoint and inputs validation or test data through it. Sampled files are saved in tmp/sampled_files.
5. calc_roc.py reads files from tmp/sampled_files, finds corresponding event files and compares them to generate ROC curves and calculate AUC.

How to use included scripts?

All python and lua scripts can be called with --help flag and list supported arguments.