The FII BCI Corpus comprises a selection of BCI databases, annotated and curated for research purposes in a collaborative project carried out at University Federico II of Naples and University Grenoble Alpes of Grenoble. Besides using this page and Zenodo API, the corpus can be easily installed using Eegle's download GUI — see Eegle.Database.downloadDB. Along with EEG data and class labels, the corpus provides comprehensive metadata that allow to select the data for the study at hand and extract relevant information. This makes it particularly easy to carry out machine learning research on BCI data — see for example Tutorial ML 2. The data curation included: discarding EEG recordings flagged by the database authors as problematic holding corrupted data (e.g., NaN values during the trials) yielding numerical problems with standard data manipulations procedures offering close-to-chance performance in standard 2-class prediction tasks, conversion of data into µV with Float32 precision, class re-labeling using a standardized scheme, downsampling (if applicable) to ≤ 256 samples per second preventing aliasing, removal of non-EEG channels, such as EOG, EMG, reference, or ground electrodes, concatenation of runs from the same session with identical experimental condition, cleaning of NaN and zero values at the beginning and ending of the recordings conversion of the cleaned data from CSV to the NY format, easily accessible in any programming language. For the details on the discarding procedures for each file see the @discarded.md file for MI and P300. For all details on the construction of the corpus, see the FII BCI Corpus Overview.