# Raw EEG data info ## Data directory structure Raw EEG data of the 10 participants. In each `.zip` file the raw EEG data is divided into the four recording sessions and into training/test partitions through the following directory structure: /sub-01 │ └───ses-01 │ └───raw_eeg_test.npy │ └───raw_eeg_training.npy │ ... │ └───ses-04 └───raw_eeg_test.npy └───raw_eeg_training.npy ## Raw EEG data files Both `raw_eeg_training.npy` and `raw_eeg_test.npy` files consist of Python dictionaries with the following keys: * `raw_eeg_data`: raw EEG data in a 2-dimensional array of shape [64 channels × N EEG samples]. The first 63 channels contain the EEG sensor data. The last channel is the simulus channel, a sparse zero vector with integers at the onset sample of each image event/trial. The raw training data has integers ranging from 1 to 16,540, one for each [training image condition][description], and the raw test data has integers ranging from 1 to 200, one for each [test image condition][description]. The target events/trials have value 99,999. * `ch_names`: names of the 63 EEG channels plus the stimulus channel. * `ch_type`: type of the 63 EEG channels ("eeg") plus the simulus channel ("stim"). * `sfreq`: EEG sampling frequency (1000Hz). * `highpass`: EEG online highpass filter (0.01Hz). * `lowpass`: EEG online lowpass filter (100Hz). ## Matching the EEG raw data with the relative image conditions To assign the EEG data events to the corresponding image conditions please refer to the [Image Set wiki][description]. ## Additional info For a detailed description of the experimental paradigm and the EEG recording protocol please refer to our [paper][paper_link]. You can also download the raw EEG data directly from [Figshare+][figshare] or [Google Drive][drive]. [paper_link]: https://doi.org/10.1016/j.neuroimage.2022.119754 [figshare]: https://plus.figshare.com/articles/dataset/A_large_and_rich_EEG_dataset_for_modeling_human_visual_object_recognition/18470912/4 [drive]: https://drive.google.com/drive/folders/1KnOcV38RthPcpZR2vtiSm0jtZ6p63RNt?usp=share_link [description]: https://osf.io/y63gw/wiki/home/