Here you can find open-source software developed by our team. Feel free to contribute!
Electroencephalography (EEG) and magnetoencephalography (MEG) measures neural activity of the brain. The signals that are recorded from multiple sensors are inherently contaminated by noise. Preprocessing aims to attenuate noise in the EEG/MEG data without removing meaningful signals in the process. The eeg-preprocessing package serves as a semiautomatic pipeline that can prepare the data for functional connectivity or event related potential (ERP) analyses. It aids researchers from the (cognitive) neuroscience community to preprocess signals without having a lot of expertise in working with EEG/MEG signals. It reduces the manual steps required to clean the data based on visual inspection, and the subjectivity in rejecting segments of data or interpolation of electrodes.
A framework for semiautomatic preprocessing of EEG/MEG data
Languages: Python, Jupyter Notebook
Alternating Serial Reaction Time Task created with jsPsych
A self-paced Alternating Serial Reaction Time Task created with the jsPsych library.
Probabilistic sequence learning task
A Process Disssociation Procedures (PDP) task created with the jsPsych library and optimized for the ASRT task.