Through invasive electrocorticographical recordings of more than 50 Parkinson's disease and epilepsy patients, performing different movement types (rotational handle, button press, hand gripping, clench and release) a movement decoding model was trained and is made publicly available through the following GitHub repository: https://github.com/neuromodulation/AcrossPatientDecodingModel. In the original publication, movement decoding was demonstrated without patient-individual training: "Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants" [1]. The machine learning model is a Ridge-Regularized Logistic Regression model, that classifies movement, based on z-score normalized and common-averaged re-referenced FFT features in eight different frequency bands. The required feature estimation can be performed through the py_neuromodulation package: https://github.com/neuromodulation/py_neuromodulation. The respective settings and pre-processing parameters, including an exemplary feature estimation pipeline, are provided in the upper GitHub repository.