Test Set Task 2

description of the test set for task 2

Provided test data

The test data for this task can be downloaded from here. The password is the same one as provided for the training set. The directory contains the following items:

  1. Preprocessed EEG test samples: The main directory contains 84 subjects’ test data in the format of subject_name.json. Note that there is no test data for sub-001. Each json file contains a python dictionary with samples IDs as keys and EEG segments of one minute as values. The preprocessed EEG segments are at 64 Hz sampling rate. You probably need to convert EEG segments from list to numpy array.

  2. raw_eeg: We also provide the corresponding raw EEG files for each subject. If you prefer to have access to the raw EEG and do your own preprocessing, then you can use this directory. The json files in this directory have exactly the same format as of the preprocessed EEG files. Sampling rate of the raw EEG is 1024 Hz.

Expected Output

As an output, participants should upload a zip file, containing json files to the following link

The files should follow the following naming convention:

task_2_group_group_name_submission _submission_number.zip

where submission_number should be 1 for the first submission and can go up to 5. We will use the latest submission as the final submission, from which the ranking will be calculated. Groups that have not yet supplied their group name, should send a mail to the organisers where they specify their name. When uploading, you will be prompted to enter a name. Please enter the name of one of the officially registered participants.

The json file(s) should contain the predicted labels for all EEG segments.Each entry in the submitted dictionary should be of the form (EEG ID) : (Reconstructed Envelope).

A correlation value of 0 will be taken in case of absent EEG ID entries. The reconstructed envelope should be of dimensions 1 x 3840 (i.e., 60 seconds of data at the prescribed sample rate of 64 Hz). We provide the script predict_test.py, which loads a pretrained model, loops over all the data of the test set, predicts the output labels and returns json files in the correct format.

For evaluation, we will calculate the mean correlation score per subject. Then, we will calculate the mean over all the subjects means for both test set 1 and test set 2 and add them to obtain a final score, which will be updated in the online leaderboard.