Resources
A list of resources that we will use during the workshop. The password to download datasets and scripts can be found here
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.
If you experience any issue when downloading the data, consider trying from a different browser.
For the workshop attendees: The main files to download are Workshop basic and the CND version of Natural speech listening below
Continuous-event Neural Data data format
Please check out our CND data format (Continuous-event Neural Data) for data sharing and standardisation. Detailed insights on the format can be found in our data-preparation guidelines.
CNSP2021 lectures and tutorials
Check out the videos of all the sessions of CNSP2021 here.and the CNSP2021 booklet for further information.
| Toolboxes | |||
| Link | Authors | Paper | Description |
| mTRF-Toolbox | Crosse, Di Liberto, Bednar and Lalor | Front Hum Neurosci 2016 | A MATLAB toolbox for relating neural signals to continuous stimuli. |
| Eelbrain | Brodbeck, Das, Kulasingham | Paper CNSP2021 slides |
A Python toolbox for relating neural signals to continuous stimuli. |
| NoiseTools | Alain de Cheveigné | Multiple references. See here | a Matlab toolbox to denoise and analyze multichannel electrophysiological data, such as from EEG, MEG, electrode arrays, optical imaging, or fMRI. |
| CNSP2021 Tutorials | |||
| Link | Authors | Paper | CND version |
| Laura Gwilliams's tutorial | Laura Gwilliams | Gwilliams & King (2020) | Demonstration of sci-kit learn for neural decoding |
| Workshop basic | Di Liberto | See data preparation guidelines document (for CNSP2021 participants only) | Workshop basic scripts, libraries, and folder structure. |
| Encoding tutorial Decoding tutorial Multivariate tutorial TRF tutorial |
Zuk Crosse Nidiffer Lalor & Crosse |
Workshop tutorial scripts (encoding, decoding, and multivariate modelling tutorials). | |
| Datasets | |||
| Link | Authors | Paper | CND version |
| Speech - multiple EEG datasets | Broderick, Andreson, Di Liberto, Crosse and Lalor | Current Biology, 2018 | Download (natural speech listening) Download (reverse speech listening) Download (cocktail party dataset) |
| Bach piano melodies - EEG dataset | Di Liberto, Pelofi, Bianco, Patel, Mehta, Herrero, de Cheveigné, Shamma and Mesgarani | eLife, 2020 | Download |
| Music listening/imagery - EEG dataset | Marion, Di Liberto, and Shamma | In press | Download CND |
| Speech listening - EEG dataset | Brennan and Hale | PLoS ONE, 2019 | Available after CNSP2021 |
| Preprocessed Speech EEG dataset | Broderick, Andreson, Di Liberto, Crosse and Lalor | Current Biology, 2018 | Download CND |
| Other Useful References | |||
| Link | Authors | Description | Year |
| Paper | Crosse, Zuk, Di Liberto, Nidiffer, Molholm, Lalor | Using TRFs in applied research. "Linear Modeling of Neurophysiological Responses to Naturalistic Stimuli: Methodological Considerations for Applied Research", Frontiers in Neuroscience | 2021 |
| Paper | Di Liberto, Nie, Yeaton, Khalighinejad, Shamma, Mesgarani | This study used multivariate encoding TRFs. "Neural representation of linguistic feature hierarchy reflects second-language proficiency", Neuroimage | 2021 |
| Paper | Di Liberto, Pelofi, Bianco, Patel, Mehta, Herrero, de Cheveigne, Shamma, Mesgarani | TRFs with music stimuli (EEG and ECoG). "Cortical encoding of melodic expectations in human temporal cortex", eLife | 2020 |