CNSP 2022 Resources
A list of resources that were used during the 2022 workshop. These resources include original and publicly available datasets that were standardised according to the CND data structure, as well as original analysis scripts and links to publicly available toolboxes for the analysis of continuous-event neural data. Note that each dataset should be used according to its own license and should be referenced as indicated by the authors in their original submission.
The main files to download, which can be taken as blueprint for new analyses, are: CNSP Resources Skeleton, which contains the folder structure and a basic tutorial code; CNSP Libraries, which should be unzipped in the 'libs' folder; the CND version of Natural speech listening and Bach piano melodies, which can also be found in the table below; and the CNSP2022 tutorials of interest listed below, which can be run on any CND dataset with minimal code modification.
|CNSP Resources Skeleton||CNSP organisers||Folder structure and a basic example TRF code||All CNSP tutorials (Day 1)|
|CNSP Libraries||CNSP organisers||Unzip them in the 'libs' folder of the skeleton||All CNSP tutorials (Day 1)|
|Video - Resource Preparation||Nidiffer, Di Liberto, and the CNSP2022 participants||This video will guide you through the CNSP resource preparation (essential practical guidelines from 5min:08s to 16min:08s)||All CNSP tutorials (Day 1)|
|Mick Crosse||Encoding and decoding models, introduction to multivariate analysis for first-time users. Please unzip in the 'CNSP/tutorials' folder||Beginner CNSP tutorial (Day 1)|
Evaluating multivariate models
Banded ridge regression
|CNSP organisers (Giovanni Di Liberto, Aaron Nidiffer, Nate Zuk)||Intermediate tutorials. Please unzip in the 'CNSP/tutorials' folder||Intermediate CNSP tutorials (Day 1)|
|Eelbrain tutorial (Python)||Joshua Kulasingham||Demonstrates data preprocessing, forward, and backward modeling with Eelbrain.
Download the zip file in the link on the left, unzip the file contents, then follow the guide to setup the tutorial: README.
For more information, see: Paper and Eelbrain documentation
|TRF Eelbrain tutorial (Day 2)|
|Envelope decoding using DNNs (Python)||Mike Thornton||Compares linear models and deep neural networks (DNN) for envelope decoding from EEG data.
The tutorial is based in Google Colab. A guide to setup the tutorial can be found in the README.
For more information, see: Paper
|DNN tutorial (Day 2)|