# Preprocessed EEG data info ## Data directory structure Preprocessed EEG data of the 10 participants. Each `.zip` file contains two files, `preprocessed_eeg_training.npy` and `preprocessed_eeg_test.npy`, corresponding to, respectively, the training and the test preprocessed EEG data partitions. ## Training data partition The `preprocessed_eeg_training.npy` file consists of a Python dictionary with the following keys: * `preprocessed_eeg_data`: preprocessed EEG training data in a 4-dimensional array of shape [[16,540 training image conditions][description] × 4 EEG repetitions × 17 EEG channels × 100 EEG time points]. * `ch_names`: names of the 17 occipital and parietal EEG channels retained during preprocessing. * `times`: the 100 time points of each EEG epoch (in seconds, with respect to stimulus onset). ## Test data partition The `preprocessed_eeg_test.npy` file consists of a Python dictionary with the following keys: * `preprocessed_eeg_data`: preprocessed EEG test data in a 4-dimensional array of shape [[200 test image conditions][description] × 80 EEG repetitions × 17 EEG channels × 100 EEG time points]. * `ch_names`: names of the 17 occipital and parietal EEG channels retained during preprocessing. * `times`: the 100 time points of each EEG epoch (in seconds, with respect to stimulus onset). ## Matching the EEG preprocessed data with the relative image conditions To assign each EEG trial/epoch to the corresponding image condition please refer to the [Image Set wiki][images_wiki]. ## Additional info For a detailed description of the preprocessing steps please refer to our [paper][paper_link], and [code][code_link]. [Here][colab] you will find a Colab interactive tutorial on how to load, visualize and link the preprocessed EEG data to the corresponding image conditions. [paper_link]: https://doi.org/10.1016/j.neuroimage.2022.119754 [code_link]: https://github.com/gifale95/eeg_encoding/tree/main/02_eeg_preprocessing [colab]: https://colab.research.google.com/drive/1i1IKeP4cK3ViscP4b4kNOVo4kRoL8tf6?usp=sharing [images_wiki]: https://osf.io/y63gw/wiki/home/