This repository hosts benchmark code and other files for the DecMeg2014 competition. The competition is about decoding the human brain from magnetoencephalographic data. For further details see: https://www.kaggle.com/c/decoding-the-human-brain .
The code is available both in Python and Matlab, with minor differences.
Initially, two basic benchmarks are available:
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benchmark_random : the code loads the files of the test set, collects the IDs of the trials in the test set and creates a valid submission file with them and by creating random class labels.
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benchmark_pooling : here the underlying idea is to ignore the differences between the pattern of brain activity of the different subjects: that they are pooled together. the code loads the files of the train subjects in the train and of the test subjects in the test set. Then it creates a simple feature space by keeping only the data of the first 0.5sec from when the stimulus starts and then concatenating all the 306 timeseries into one feature vector. After a simple z-scoring of each feature, a linear classifier is trained on the train set and the class labels of the test set are predicted. A valid submission file is created from the predicted class labels.
To run the benchmarks,
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Download and unzip the train data and the test data from the competition website. Note that not all the training data are necessary to run the benchmarks.
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Download the code of this repository.
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For Python: enter the "python" directory and run
python benchmark_random.pyorpython benchmark_pooling.py. The pooling benchmark may take several minutes to run, depending on the number of input subjects you specify. The data are expected to be in "python/data/".
Each benchmark creates a file "submission.csv".